Journal articles on the topic 'Sorted nearest neighborhood clustering'
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Lei, Jing. "An Analytical Model of College Students’ Mental Health Education Based on the Clustering Algorithm." Mathematical Problems in Engineering 2022 (September 19, 2022): 1–11. http://dx.doi.org/10.1155/2022/1880214.
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This paper proposes an improved k-means clustering algorithm to analyze the mental health education of college students. It offers an improved k-means clustering algorithm with optimized centroid selection to address the problems of randomly selected class cluster centroids that lead to inconsistent algorithm results and easily fall into local optimal solutions of the traditional k-means clustering algorithm. The algorithm determines the neighborhood parameter based on the Euclidean distance between the data object and its nearest neighbor in the data set. It counts the object density based on the neighborhood parameter Eps. In the initial class cluster centroid selection phase, the algorithm randomly selects the first-class cluster centroid, and subsequent class cluster centroids are chosen based on the data object density information and the distance information between the data object and the existing class cluster centroids. The proposed improved k-means clustering algorithm and clustering validity metrics are tested using several simulated and real datasets. In this paper, the characteristics and application areas of the improved k-means clustering algorithm are sorted out, the self-determination theory related to the enhanced k-means clustering algorithm is investigated, and the behavior of the improved k-means clustering algorithm in the enhanced k-means clustering algorithm system and the octagonal behavior analysis method is also sorted out through the improved k-means clustering algorithm mental health management cases. The path of intervention in mental health education is designed through the improved k-means clustering algorithm. The intervention points are explained, including motivation discovery, mechanism setting, and component matching of the enhanced k-means clustering algorithm.
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OHUCHI, Takao, Tadahide KATO, and Masato KANEKO. "Speed Control of Robot Vehicle Using Fuzzy Nearest Neighborhood Clustering." Journal of Japan Society for Fuzzy Theory and Systems 12, no. 1 (2000): 143–52. http://dx.doi.org/10.3156/jfuzzy.12.1_143.
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Deepa, A. Roslin, and Ramalingam Sugumar. "Efficient Query Service Provider using Clustering K-Nearest Neighborhood Algorithm." International Journal of Computer Trends and Technology 36, no. 4 (June 25, 2016): 176–82. http://dx.doi.org/10.14445/22312803/ijctt-v36p132.
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Lin, Ruisen, Li Gao, and Tielu Yin. "Adaptive fuzzy controller using nearest neighborhood clustering and its application." Journal of Shanghai University (English Edition) 3, no. 1 (March 1999): 53–57. http://dx.doi.org/10.1007/s11741-999-0029-0.
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Yu, Qingying, Chuanming Chen, Liping Sun, and Xiaoyao Zheng. "Urban Hotspot Area Detection Using Nearest-Neighborhood-Related Quality Clustering on Taxi Trajectory Data." ISPRS International Journal of Geo-Information 10, no. 7 (July 10, 2021): 473. http://dx.doi.org/10.3390/ijgi10070473.
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Urban hotspot area detection is an important issue that needs to be explored for urban planning and traffic management. It is of great significance to mine hotspots from taxi trajectory data, which reflect residents’ travel characteristics and the operational status of urban traffic. The existing clustering methods mainly concentrate on the number of objects contained in an area within a specified size, neglecting the impact of the local density and the tightness between objects. Hence, a novel algorithm is proposed for detecting urban hotspots from taxi trajectory data based on nearest neighborhood-related quality clustering techniques. The proposed spatial clustering algorithm not only considers the maximum clustering in a limited range but also considers the relationship between each cluster center and its nearest neighborhood, effectively addressing the clustering issue of unevenly distributed datasets. As a result, the proposed algorithm obtains high-quality clustering results. The visual representation and simulated experimental results on a real-life cab trajectory dataset show that the proposed algorithm is suitable for inferring urban hotspot areas, and that it obtains better accuracy than traditional density-based methods.
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Irum, Misbah, and Ejaz Muhammad. "Fuzzy Logic Based Time Series Prediction Algorithm Using Nearest Neighborhood Clustering." Journal of Engineering Research 8, no. 3 (August 13, 2020): 135–52. http://dx.doi.org/10.36909/jer.v8i3.8062.
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Lin, Jun-Lin, Jen-Chieh Kuo, and Hsing-Wang Chuang. "Improving Density Peak Clustering by Automatic Peak Selection and Single Linkage Clustering." Symmetry 12, no. 7 (July 14, 2020): 1168. http://dx.doi.org/10.3390/sym12071168.
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Density peak clustering (DPC) is a density-based clustering method that has attracted much attention in the academic community. DPC works by first searching density peaks in the dataset, and then assigning each data point to the same cluster as its nearest higher-density point. One problem with DPC is the determination of the density peaks, where poor selection of the density peaks could yield poor clustering results. Another problem with DPC is its cluster assignment strategy, which often makes incorrect cluster assignments for data points that are far from their nearest higher-density points. This study modifies DPC and proposes a new clustering algorithm to resolve the above problems. The proposed algorithm uses the radius of the neighborhood to automatically select a set of the likely density peaks, which are far from their nearest higher-density points. Using the potential density peaks as the density peaks, it then applies DPC to yield the preliminary clustering results. Finally, it uses single-linkage clustering on the preliminary clustering results to reduce the number of clusters, if necessary. The proposed algorithm avoids the cluster assignment problem in DPC because the cluster assignments for the potential density peaks are based on single-linkage clustering, not based on DPC. Our performance study shows that the proposed algorithm outperforms DPC for datasets with irregularly shaped clusters.
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She, Chunyan, and Shaohua Zeng. "An efficient local outlier detection optimized by rough clustering." Journal of Intelligent & Fuzzy Systems 42, no. 3 (February 2, 2022): 2071–82. http://dx.doi.org/10.3233/jifs-211433.
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Outlier detection is a hot issue in data mining, which has plenty of real-world applications. LOF (Local Outlier Factor) can capture the abnormal degree of objects in the dataset with different density levels, and many extended algorithms have been proposed in recent years. However, the LOF needs to search the nearest neighborhood of each object on the whole dataset, which greatly increases the time cost. Most of these extended algorithms only consider the distance between an object and its neighborhood, but ignore the local distribution of an object within its neighborhood, resulting in a high false-positive rate. To improve the running speed, a rough clustering based on triple fusion is proposed, which divides a dataset into several subsets and outlier detection is performed only on each subset. Then, considering the local distribution of an object within its neighborhood, a new local outlier factor is constructed to estimate the abnormal degree of each object. Finally, the experimental results indicate that the proposed algorithm has better performance and lower running time than the others.
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Liu, Yaohui, Dong Liu, Fang Yu, and Zhengming Ma. "A Double-Density Clustering Method Based on “Nearest to First in” Strategy." Symmetry 12, no. 5 (May 6, 2020): 747. http://dx.doi.org/10.3390/sym12050747.
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The existing density clustering algorithms have high error rates on processing data sets with mixed density clusters. For overcoming shortcomings of these algorithms, a double-density clustering method based on Nearest-to-First-in strategy, DDNFC, is proposed, which calculates two densities for each point by using its reverse k nearest neighborhood and local spatial position deviation, respectively. Points whose densities are both greater than respective average densities of all points are core. By searching the strongly connected subgraph in the graph constructed by the core objects, the data set is clustered initially. Then each non-core object is classified to its nearest cluster by using a strategy dubbed as ‘Nearest-to-First-in’: the distance of each unclassified point to its nearest cluster calculated firstly; only the points with the minimum distance are placed to their nearest cluster; this procedure is repeated until all unclassified points are clustered or the minimum distance is infinite. To test the proposed method, experiments on several artificial and real-world data sets are carried out. The results show that DDNFC is superior to the state-of-art methods like DBSCAN, DPC, RNN-DBSCAN, and so on.
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Thompson, Amy E., John P. Walden, Adrian S. Z. Chase, Scott R. Hutson, Damien B. Marken, Bernadette Cap, Eric C. Fries, et al. "Ancient Lowland Maya neighborhoods: Average Nearest Neighbor analysis and kernel density models, environments, and urban scale." PLOS ONE 17, no. 11 (November 2, 2022): e0275916. http://dx.doi.org/10.1371/journal.pone.0275916.
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Many humans live in large, complex political centers, composed of multi-scalar communities including neighborhoods and districts. Both today and in the past, neighborhoods form a fundamental part of cities and are defined by their spatial, architectural, and material elements. Neighborhoods existed in ancient centers of various scales, and multiple methods have been employed to identify ancient neighborhoods in archaeological contexts. However, the use of different methods for neighborhood identification within the same spatiotemporal setting results in challenges for comparisons within and between ancient societies. Here, we focus on using a single method—combining Average Nearest Neighbor (ANN) and Kernel Density (KD) analyses of household groups—to identify potential neighborhoods based on clusters of households at 23 ancient centers across the Maya Lowlands. While a one-size-fits all model does not work for neighborhood identification everywhere, the ANN/KD method provides quantifiable data on the clustering of ancient households, which can be linked to environmental zones and urban scale. We found that centers in river valleys exhibited greater household clustering compared to centers in upland and escarpment environments. Settlement patterns on flat plains were more dispersed, with little discrete spatial clustering of households. Furthermore, we categorized the ancient Maya centers into discrete urban scales, finding that larger centers had greater variation in household spacing compared to medium-sized and smaller centers. Many larger political centers possess heterogeneity in household clustering between their civic-ceremonial cores, immediate hinterlands, and far peripheries. Smaller centers exhibit greater household clustering compared to larger ones. This paper quantitatively assesses household clustering among nearly two dozen centers across the Maya Lowlands, linking environment and urban scale to settlement patterns. The findings are applicable to ancient societies and modern cities alike; understanding how humans form multi-scalar social groupings, such as neighborhoods, is fundamental to human experience and social organization.
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Agarkhed, Jayashree, Vijayalaxmi Kadrolli, and Siddarama R. Patil. "Fuzzy Clustering with Multi-Constraint QoS Service Routing in Wireless Sensor Networks." Journal of Telecommunications and Information Technology 1 (March 29, 2019): 31–38. http://dx.doi.org/10.26636/jtit.2019.127818.
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This paper presents a fuzzy logic-based, service differentiated, QoS aware routing protocol (FMSR) offering multipath routing for WSNs, with the purpose of providing a service differentiated path meant for communication between nodes, based on actual requirements. The proposed protocol initially forms a cluster by fuzzy c-means. Next, the building of a routing follows, so as to establish multiple paths between nodes through the modified QoS k-nearest neighborhood, based on different QoS constraints and on optimum shortest paths. If one node in the path fails due to lack of residual energy, bandwidth, packet loss, delay, an alternate path leading through another neighborhood node is selected for communication. Simulation results show that the proposed protocol performs better in terms of packet delivery ratio, delay, packet drop ratio and throughput compared to other existing routing protocols.
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WANG, JING, and LILI RONG. "SIMILARITY INDEX BASED ON THE INFORMATION OF NEIGHBOR NODES FOR LINK PREDICTION OF COMPLEX NETWORK." Modern Physics Letters B 27, no. 06 (February 6, 2013): 1350039. http://dx.doi.org/10.1142/s0217984913500395.
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Link prediction in complex networks has attracted much attention recently. Many local similarity measures based on the measurements of node similarity have been proposed. Among these local similarity indices, the neighborhood-based indices Common Neighbors (CN), Adamic-Adar (AA) and Resource Allocation (RA) index perform best. It is found that the node similarity indices required only information on the nearest neighbors are assigned high scores and have very low computational complexity. In this paper, a new index based on the contribution of common neighbor nodes to edges is proposed and shown to have competitively good or even better prediction than other neighborhood-based indices especially for the network with low clustering coefficient with its high efficiency and simplicity.
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Sheng, Xiang-Rong, De-Chuan Zhan, Su Lu, and Yuan Jiang. "Multi-View Anomaly Detection: Neighborhood in Locality Matters." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4894–901. http://dx.doi.org/10.1609/aaai.v33i01.33014894.
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Identifying anomalies in multi-view data is a difficult task due to the complicated data characteristics of anomalies. Specifically, there are two types of anomalies in multi-view data–anomalies that have inconsistent features across multiple views and anomalies that are consistently anomalous in each view. Existing multi-view anomaly detection approaches have some issues, e.g., they assume multiple views of a normal instance share consistent and normal clustering structures while anomaly exhibits anomalous clustering characteristics across multiple views. When there are no clusters in data, it is difficult for existing approaches to detect anomalies. Besides, existing approaches construct a profile of normal instances, then identify instances that do not conform to the normal profile as anomalies. The objective is formulated to profile normal instances, but not to estimate the set of normal instances, which results in sub-optimal detectors. In addition, the model trained to profile normal instances uses the entire dataset including anomalies. However, anomalies could undermine the model, i.e., the model is not robust to anomalies. To address these issues, we propose the nearest neighborbased MUlti-View Anomaly Detection (MUVAD) approach. Specifically, we first propose an anomaly measurement criterion and utilize this criterion to formulate the objective of MUVAD to estimate the set of normal instances explicitly. We further develop two concrete relaxations for implementing the MUVAD as MUVAD-QPR and MUVAD-FSR. Experimental results validate the superiority of the proposed MUVAD approaches.
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Tang, Chunhua, Han Wang, Zhiwen Wang, Xiangkun Zeng, Huaran Yan, and Yingjie Xiao. "An improved OPTICS clustering algorithm for discovering clusters with uneven densities." Intelligent Data Analysis 25, no. 6 (October 29, 2021): 1453–71. http://dx.doi.org/10.3233/ida-205497.
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Most density-based clustering algorithms have the problems of difficult parameter setting, high time complexity, poor noise recognition, and weak clustering for datasets with uneven density. To solve these problems, this paper proposes FOP-OPTICS algorithm (Finding of the Ordering Peaks Based on OPTICS), which is a substantial improvement of OPTICS (Ordering Points To Identify the Clustering Structure). The proposed algorithm finds the demarcation point (DP) from the Augmented Cluster-Ordering generated by OPTICS and uses the reachability-distance of DP as the radius of neighborhood eps of its corresponding cluster. It overcomes the weakness of most algorithms in clustering datasets with uneven densities. By computing the distance of the k-nearest neighbor of each point, it reduces the time complexity of OPTICS; by calculating density-mutation points within the clusters, it can efficiently recognize noise. The experimental results show that FOP-OPTICS has the lowest time complexity, and outperforms other algorithms in parameter setting and noise recognition.
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Chang, Ray-I., Shu-Yu Lin, Jan-Ming Ho, Chi-Wen Fann, and Yu-Chun Wang. "A novel content based image retrieval system using K-means/KNN with feature extraction." Computer Science and Information Systems 9, no. 4 (2012): 1645–61. http://dx.doi.org/10.2298/csis120122047c.
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Image retrieval has been popular for several years. There are different system designs for content based image retrieval (CBIR) system. This paper propose a novel system architecture for CBIR system which combines techniques include content-based image and color analysis, as well as data mining techniques. To our best knowledge, this is the first time to propose segmentation and grid module, feature extraction module, K-means and k-nearest neighbor clustering algorithms and bring in the neighborhood module to build the CBIR system. Concept of neighborhood color analysis module which also recognizes the side of every grids of image is first contributed in this paper. The results show the CBIR systems performs well in the training and it also indicates there contains many interested issue to be optimized in the query stage of image retrieval.
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Li, Jingjing. "Application of Intelligent Fuzzy Decision Tree Algorithm in English Teaching Model Improvement." Complexity 2021 (August 14, 2021): 1–10. http://dx.doi.org/10.1155/2021/8631019.
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As the number of students in universities continues to grow, the university academic management system has a large amount of data on student performance. However, the utilization of these data is only limited to simple query and statistical work, and there is no precedent of using these data for improving English teaching mode. With the application of fuzzy theory in machine learning and artificial intelligence, the fuzzy decision tree algorithm was born by integrating fuzzy set theory with decision tree algorithm. In this paper, we propose a way to obtain the centroids of continuous attribute clustering by K-means algorithm and combine the triangular fuzzy number to fuzzy the continuous data. In addition, this paper analyzes the influence of nearest neighbor distance on classification, introduces Gaussian weight function, gives different voting weights to the neighborhood according to the distance, and establishes a weighted K-nearest neighbor classification algorithm. To address the problem of low classification efficiency of K-nearest neighbor algorithm when the dataset is large, this paper further improves the algorithm and establishes the partitioned weighted K-nearest neighbor algorithm. The classification time was shortened from 11.39 seconds to 5.22 seconds, and the classification efficiency greatly improved.
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Zhong, Qixue, Yuansheng Liu, Xiaoxiao Guo, and Lijun Ren. "Dynamic Obstacle Detection and Tracking Based on 3D Lidar." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 5 (September 20, 2018): 602–10. http://dx.doi.org/10.20965/jaciii.2018.p0602.
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Detection and tracking of dynamic obstacle is one of the research hotspot in autonomous vehicles. In this paper, a dynamic obstacle detection and tracking method based on 3D lidar is proposed. The nearest neighborhood method is used to cluster the data obtained by the laser lidar. The characteristic parameters of the clustering obstacles are analyzed. Multiple hypothesis tracking model (MHT) algorithm and the nearest neighbor association algorithm are used for data association of two consecutive frames of obstacle information. The dynamic and static state of obstacles are analyzed through the temporal and spatial correlation of the obstacle. Finally, we use linear Kalman filter to predict the movement state of the obstacle. The experimental results on a low-speed driverless vehicle “small whirlwind” which is an autonomous sightseeing vehicle show that the method can accurately detect the dynamic obstacles in unknown environment with effectiveness and real-time performance.
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Rezaei, Zahra, Ali Selamat, Arash Taki, Mohd Shafry Mohd Rahim, and Mohammed Rafiq Abdul Kadir. "Automatic plaque segmentation based on hybrid fuzzy clustering and k nearest neighborhood using virtual histology intravascular ultrasound images." Applied Soft Computing 53 (April 2017): 380–95. http://dx.doi.org/10.1016/j.asoc.2016.12.048.
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Tanbo, Masaya, Ryoma Nojiri, Yuusuke Kawakita, and Haruhisa Ichikawa. "Active RFID Attached Object Clustering Method with New Evaluation Criterion for Finding Lost Objects." Mobile Information Systems 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/3637814.
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An active radio frequency identification (RFID) tag that can communicate with smartphones using Bluetooth low energy technology has recently received widespread attention. We have studied a novel approach to finding lost objects using active RFID. We hypothesize that users can deduce the location of a lost object from information about surrounding objects in an environment where RFID tags are attached to all personal belongings. To help find lost objects from the proximity between RFID tags, the system calculates the proximity between pairs of RFID tags from the RSSI series and estimates the groups of objects in the neighborhood. We developed a method for calculating the proximity of the lost object to those around it using a distance function between RSSI series and estimating the group by hierarchical clustering. There is no method to evaluate whether a combination is suitable for application purposes directly. Presently, different combinations of distance functions and clustering algorithms yield different clustering results. Thus, we propose the number of nearest neighbor candidates (NNNC) as the criterion to evaluate the clustering results. The simulation results show that the NNNC is an appropriate evaluation criterion for our system because it is able to exhaustively evaluate the combination of distance functions and clustering algorithms.
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Moranta, Leslie, Jonathan Gagné, Dominic Couture, and Jacqueline K. Faherty. "New Coronae and Stellar Associations Revealed by a Clustering Analysis of the Solar Neighborhood." Astrophysical Journal 939, no. 2 (November 1, 2022): 94. http://dx.doi.org/10.3847/1538-4357/ac8c25.
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Abstract We present the results of a density-based clustering analysis of the 6D XYZ Galactic positions and UVW-space velocities of nearby (≤200 pc) Gaia Early Data Release 3 stars with radial velocities using HDBSCAN, in opposition to previous studies (Kounkel & Covey 2019; Meingast et al. 2021) that only included positions and tangential velocities. Among the 241 recovered clusters, we identify more than 50 known associations, 32 new candidate stellar streams aged 100 Myr to 3 Gyr, nine extensions of known Theia groups uncovered by Kounkel & Covey, and eight newly recognized coronae around nearby open clusters. Three confirmed exoplanet-hosting stars and three more TESS transiting exoplanet candidates are part of the new groups discovered here, including TOI–1807 and TOI–2076 from Hedges et al. (2021) that were suspected to belong to a yet unidentified moving group. The new groups presented here were not previously recognized because of their older ages, low spatial density, and projection effects that spread out the tangential velocities of their nearby comoving members. Several newly identified structures reach distances within 60 pc of the Sun, providing new grounds for the identification of isolated planetary-mass objects. The nearest member of the newly recognized corona of Volans–Carina is V419 Hya, a known young debris disk star at a distance of 22 pc. This study outlines the importance of further characterization of young associations in the immediate solar neighborhood, which will provide new laboratories for the precise age calibration of nearby stars, exoplanets, and substellar objects.
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Radiah Shariff, S. Sarifah, Hamdan Abdul Maad, Nursyaza Narsuha Abdul Halim, and Zuraidah Derasit. "Determining Hotspots of Road Accidents using Spatial Analysis." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 1 (January 1, 2018): 146. http://dx.doi.org/10.11591/ijeecs.v9.i1.pp146-151.
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<p>Road accidents continuously become a major problem in Malaysia and consequently cause loss of life or property. Due to that, many road accident data have been collected by highway concessionaries or build–operate–transfer operating companies in the country meant for coming up with proper counter measures. Several analyses can be done on the accumulated data in order to improve road safety. In this study the reported road accidents cases in North South Expressway (NSE) from Sungai Petani to Bukit Lanjan during 2011 to 2014 period is analyzed. The aim is to determine whether the pattern is clustered at certain area and to identify spatial pattern of hot spots across this longest controlled-access expressway in Malaysia as hotspot represents the location of the road which is considered high risk and the probability of traffic accidents in relation to the level of risk in the surrounding areas. As no methodology for identifying hotspot has been agreed globally yet; hence this study helped determining the suitable principles and techniques for determination of the hotspot on Malaysian highways. Two spatial analysis techniques were applied, Nearest Neighborhood Hierarchical (NNH) Clustering and Spatial Temporal Clustering, using CrimeStat® and visualizing in ArcGIS™ software to calculate the concentration of the incidents and the results are compared based on their accuracies. Results identified several hotspots and showed that they varied in number and locations, depending on their parameter values. Further analysis on selected hot spot location showed that Spatial Temporal Clustering (STAC) has a higher accuracy index compared to Nearest Neighbor Hierarchical Clustering (NNH). Several recommendations on counter measures have also been proposed based on the details results.</p>
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Wang, Xiaowei. "Research on Hybrid Immune Algorithm for Solving the Location-Routing Problem With Simultaneous Pickup and Delivery." Journal of Cases on Information Technology 24, no. 5 (February 21, 2022): 1–17. http://dx.doi.org/10.4018/jcit.295253.
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In the simultaneous pickup and delivery problem, every customer has both delivery demand and pick-up demand, and both demands need to be served simultaneously．Under this condition, a location-routing problem with simultaneous pickup and delivery model was established to minimize the sum of location cost, routing cost and transportation cost. For solving this model, a Hybrid Immune Algorithm was developed. The initial solution was generated by greedy clustering algorithm; The antibody was evaluated and sorted by the original immune algorithm; And the immune operation of the original algorithm was improved by the neighborhood search operation. Finally, the feasibility of the model and the effectiveness of the algorithm were verified by using the Hybrid Immune Algorithm, the original Immune Algorithm, the simulated annealing algorithm and the ant colony algorithm.
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Miao, Xia, Ziyao Yu, and Ming Liu. "Using Partial Differential Equation Face Recognition Model to Evaluate Students’ Attention in a College Chinese Classroom." Advances in Mathematical Physics 2021 (October 11, 2021): 1–10. http://dx.doi.org/10.1155/2021/3950445.
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The partial differential equation learning model is applied to another high-level visual-processing problem: face recognition. A novel feature selection method based on partial differential equation learning model is proposed. The extracted features are invariant to rotation and translation and more robust to illumination changes. In the evaluation of students’ concentration in class, this paper firstly uses the face detection algorithm in face recognition technology to detect the face and intercept the expression data, and calculates the rise rate. Then, the improved model of concentration analysis and evaluation of a college Chinese class is used to recognize facial expression, and the corresponding weight is given to calculate the expression score. Finally, the head-up rate calculated at the same time is multiplied by the expression score as the final concentration score. Through the experiment and analysis of the experimental results in the actual classroom, the corresponding conclusions are drawn and teaching suggestions are provided for teachers. For each face, a large neighborhood set is firstly selected by the k -nearest neighbor method, and then, the sparse representation of sample points in the neighborhood is obtained, which effectively combines the locality of k -nearest neighbor and the robustness of sparse representation. In the sparse preserving nonnegative block alignment algorithm, a discriminant partial optimization model is constructed by using sparse reconstruction coefficients to describe local geometry and weighted distance to describe class separability. The two algorithms obtain good clustering and recognition results in various cases of real and simulated occlusion, which shows the effectiveness and robustness of the algorithm. In order to verify the reliability of the model, this paper verified the model through in-class practice tests, teachers’ questions, and interviews with students and teachers. The results show that the proposed joint evaluation method based on expression and head-up rate has high accuracy and reliability.
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Bobadilla, Jesús, Ángel González-Prieto, Fernando Ortega, and Raúl Lara-Cabrera. "Deep learning approach to obtain collaborative filtering neighborhoods." Neural Computing and Applications 34, no. 4 (October 5, 2021): 2939–51. http://dx.doi.org/10.1007/s00521-021-06493-7.
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AbstractIn the context of recommender systems based on collaborative filtering (CF), obtaining accurate neighborhoods of the items of the datasets is relevant. Beyond particular individual recommendations, knowing these neighbors is fundamental for adding differentiating factors to recommendations, such as explainability, detecting shilling attacks, visualizing item relations, clustering, and providing reliabilities. This paper proposes a deep learning architecture to efficiently and accurately obtain CF neighborhoods. The proposed design makes use of a classification neural network to encode the dataset patterns of the items, followed by a generative process that obtains the neighborhood of each item by means of an iterative gradient localization algorithm. Experiments have been conducted using five popular open datasets and five representative baselines. The results show that the proposed method improves the quality of the neighborhoods compared to the K-Nearest Neighbors (KNN) algorithm for the five selected similarity measure baselines. The efficiency of the proposed method is also shown by comparing its computational requirements with that of KNN.
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Zhang, Xian-xia, Jun-da Qi, Bai-li Su, Shi-wei Ma, and Hong-bo Liu. "A Clustering and SVM Regression Learning-Based Spatiotemporal Fuzzy Logic Controller with Interpretable Structure for Spatially Distributed Systems." Journal of Applied Mathematics 2012 (2012): 1–24. http://dx.doi.org/10.1155/2012/841609.
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Many industrial processes and physical systems are spatially distributed systems. Recently, a novel 3-D FLC was developed for such systems. The previous study on the 3-D FLC was concentrated on an expert knowledge-based approach. However, in most of situations, we may lack the expert knowledge, while input-output data sets hidden with effective control laws are usually available. Under such circumstance, a data-driven approach could be a very effective way to design the 3-D FLC. In this study, we aim at developing a new 3-D FLC design methodology based on clustering and support vector machine (SVM) regression. The design consists of three parts: initial rule generation, rule-base simplification, and parameter learning. Firstly, the initial rules are extracted by a nearest neighborhood clustering algorithm with Frobenius norm as a distance. Secondly, the initial rule-base is simplified by merging similar 3-D fuzzy sets and similar 3-D fuzzy rules based on similarity measure technique. Thirdly, the consequent parameters are learned by a linear SVM regression algorithm. Additionally, the universal approximation capability of the proposed 3-D fuzzy system is discussed. Finally, the control of a catalytic packed-bed reactor is taken as an application to demonstrate the effectiveness of the proposed 3-D FLC design.
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Hsue, Albert Wen Jeng. "Improvement of Wire-EDM Finishing Processes through Adaptive Fuzzy Logic Control Algorithm." Materials Science Forum 594 (August 2008): 407–14. http://dx.doi.org/10.4028/www.scientific.net/msf.594.407.
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This paper presented tries to improve finishing accuracy through monitoring the lateral gap-width by an adaptive fuzzy logic controller. Since the complexities and stochastic properties inherited in EDM processes, it seems promising to introduce soft computing algorithm in such a process control application. The adaptive fuzzy logic system (AFLS) composed of a fuzzy logic system and a nearest neighborhood-clustering algorithm, which adjusts not only the parameters of fuzzy system but also its own structure. A detailed material removal mechanism was established through the concept of discharge-angle for analysis on the machining processes. Formation of lateral gap width was analyzed through material removal swept by the discharge angle. Combined with discharge power and other operation conditions, a feasible gap-width estimation model was established. And an adaptive control based on the AFLS was proposed to improve the conventional servo-voltage strategy in both the machining accuracy and the stability aspects. Experiment results showed that the proposed estimation model is effective, and the proposed control strategy help significantly in improving the geometric accuracy of Wire-EDM’s finishing process.
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Zhang, Zhihao. "A Method of Recommending Physical Education Network Course Resources Based on Collaborative Filtering Technology." Scientific Programming 2021 (October 28, 2021): 1–9. http://dx.doi.org/10.1155/2021/9531111.
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Through the current research on e-learning, it is found that the present e-learning system applied to the recommendation activities of learning resources has only two search methods: Top-N and keywords. These search methods cannot effectively recommend learning resources to learners. Therefore, the collaborative filtering recommendation technology is applied, in this paper, to the process of personalized recommendation of learning resources. We obtain user content and functional interest and predict the comprehensive interest of web and big data through an infinite deep neural network. Based on the collaborative knowledge graph and the collaborative filtering algorithm, the semantic information of teaching network resources is extracted from the collaborative knowledge graph. According to the principles of the nearest neighbor recommendation, the course attribute value preference matrix (APM) is obtained first. Next, the course-predicted values are sorted in descending order, and the top T courses with the highest predicted values are selected as the final recommended course set for the target learners. Each course has its own online classroom; the teacher will publish online class details ahead of time, and students can purchase online access to the classroom number and password. The experimental results show that the optimal number of clusters k is 9. Furthermore, for extremely sparse matrices, the collaborative filtering technique method is more suitable for clustering in the transformed low-dimensional space. The average recommendation satisfaction degree of collaborative filtering technology method is approximately 43.6%, which demonstrates high recommendation quality.
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CHATZIKONSTANTINOU, Ioanna, and Stavroula KATSIAMPOURA. "Spatiotemporal patterns of commercial activities in Exarchia-Neapoli area." European Journal of Geography 13, no. 2 (April 12, 2022): 079–107. http://dx.doi.org/10.48088/ejg.i.cha.13.2.079.107.
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The focus of this paper is the analysis of spatio-temporal patterns of commercial activities in the area of Exarchia-Neapoli, in Athens, during the last decade. A significant downturn during the financial crisis has left its mark on commerce along with all other sectors of the economy in Greece. However, there is poor geographical analysis up to this date, depicting the changes in spatial concentration and dispersion of shops, flourishing and decaying sectors and their spatial footprint. The main purpose of this study is to reveal these transformations in a central mixed-use neighborhood of Athens. The type of commercial activity of ground floor stores and offices in the area is recorded at three different points in time (2009, 2014, 2019). After the collection and categorization of data, spatial analysis was carried out using geostatistical indicators such as mean center and standard deviational ellipse and spatial patterns were determined through nearest neighbor analysis. Τhe analysis over space and time reveals trends and patterns of clustering and avoidance respectively, both within commercial uses and between different ones. Furthermore, their correlation with the centrality of the network (space syntax analysis) reveals not only proximity relationships, but also their strength and resilience over time. Finally, we discuss our findings in relation to the dynamics of commerce in the Exarchia area and propose scaled-up research opportunities.
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Molina-García, Javier, Cristina Menescardi, Isaac Estevan, and Ana Queralt. "Associations between Park and Playground Availability and Proximity and Children’s Physical Activity and Body Mass Index: The BEACH Study." International Journal of Environmental Research and Public Health 19, no. 1 (December 27, 2021): 250. http://dx.doi.org/10.3390/ijerph19010250.
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A cross-sectional study was designed to evaluate the relationship between the availability and proximity to parks and playgrounds and physical activity (PA). Moreover, the accessibility to parks and playgrounds and its association with active commuting to/from school (ACS) and body mass index (BMI) were analyzed. The sample was composed of children aged 6–12 years old from the BEACH (Built Environment and Active CHildren) study in Valencia, Spain. The availability and proximity to parks and playgrounds were calculated at different buffer sizes (250, 500, 1000 and 1250 m) using geographical information system data. PA out of school was assessed using accelerometers. Sociodemographics and ACS were measured with a parent questionnaire. Objectively measured weight and height were used to calculate BMI. Mixed linear regression analyses were conducted for each exposure variable, adjusting for sociodemographics, neighborhood walkability level, and participant clustering. The number of parks and playgrounds were positively associated with moderate to vigorous PA (MVPA) and total PA (TPA); including light PA and MVPA, during weekdays, in different buffer sizes. A negative relationship between distance to the nearest playground and TPA during weekdays was found. In addition, the number of playgrounds was positively related to ACS in different buffer sizes, whereas park land area was negatively related to the BMI percentile. This study highlights the importance of assessing the availability and proximity to parks and playgrounds in children’s neighborhoods when PA behavior and weight status are analyzed. Study findings may help policymakers when targeting interventions to promote health-enhancing behaviors in children.
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Huang, Yongdi, Qionghai Chen, Zhiyu Zhang, Ke Gao, Anwen Hu, Yining Dong, Jun Liu, and Lihong Cui. "A Machine Learning Framework to Predict the Tensile Stress of Natural Rubber: Based on Molecular Dynamics Simulation Data." Polymers 14, no. 9 (May 6, 2022): 1897. http://dx.doi.org/10.3390/polym14091897.
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Natural rubber (NR), with its excellent mechanical properties, has been attracting considerable scientific and technological attention. Through molecular dynamics (MD) simulations, the effects of key structural factors on tensile stress at the molecular level can be examined. However, this high-precision method is computationally inefficient and time-consuming, which limits its application. The combination of machine learning and MD is one of the most promising directions to speed up simulations and ensure the accuracy of results. In this work, a surrogate machine learning method trained with MD data is developed to predict not only the tensile stress of NR but also other mechanical behaviors. We propose a novel idea based on feature processing by combining our previous experience in performing predictions of small samples. The proposed ML method consists of (i) an extreme gradient boosting (XGB) model to predict the tensile stress of NR, and (ii) a data augmentation algorithm based on nearest-neighbor interpolation (NNI) and the synthetic minority oversampling technique (SMOTE) to maximize the use of limited training data. Among the data enhancement algorithms that we design, the NNI algorithm finally achieves the effect of approaching the original data sample distribution by interpolating at the neighborhood of the original sample, and the SMOTE algorithm is used to solve the problem of sample imbalance by interpolating at the clustering boundaries of minority samples. The augmented samples are used to establish the XGB prediction model. Finally, the robustness of the proposed models and their predictive ability are guaranteed by high performance values, which indicate that the obtained regression models have good internal and external predictive capacities.
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Dhapola, Parashar, Mikael Sommarin, Mohamed Eldeeb, Amol Ugale, David Bryder, and Göran Karlsson. "Transcriptome Based Projection of Single Cells to Uncover Development and Heterogeneity of Abnormal Hematopoietic Cells." Blood 134, Supplement_1 (November 13, 2019): 2520. http://dx.doi.org/10.1182/blood-2019-130587.
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Single-cell transcriptomics (scRNA-Seq) has accelerated the investigation of hematopoietic differentiation. Based on scRNA-Seq data, more refined models of lineage determination in stem- and progenitor cells are now available. Despite such advances, characterizing leukemic cells using single-cell approaches remains challenging. The conventional strategies of scRNA-Seq analysis map all cells on the same low dimensional space using approaches like tSNE and UMAP. However, when used for comparing normal and leukemic cells, such methods are often inadequate as the transcriptome of the leukemic cells has systematically diverged, resulting in irrelevant separation of leukemic subpopulations from their healthy counterpart. Here, we have developed a new computational approach bundled into a tool called Nabo (nabo.readthedocs.io) that has the capacity to directly compare cells that are otherwise unalignable. First, Nabo creates a shared nearest neighbor graph of the reference population, and the heterogeneity of this population is subsequently defined by performing clustering on the graph and calculating a low dimensional representation using t-SNE or UMAP. Nabo then calculates the similarity of incoming cells from a target population to each cell in the reference graph using a modified Canberra metric. The reference cells with higher similarity to the target cells obtain higher mapping scores. The built-in classifier is used to assign each target cell a reference cluster identity. We tested Nabo's accuracy on control datasets and found that Nabo's performance in terms of accuracy and robustness of projection is comparable to state-of-art methods. Moreover, Nabo is a generalized domain adaptation algorithm and hence can perform classification of target cells that are arbitrarily dissimilar to reference cells. Nabo could identify the cell-identity of sorted CD19+ B cells, CD14+ monocytes and CD56+ by projecting these unlabeled cells onto labelled peripheral blood mononuclear cells with an average specificity higher than 0.98. The general applicability of Nabo was demonstrated by successfully integrating pancreatic cells, sequenced in three different studies using different sequencing chemistries with comparable or better accuracy than existing methods. Also, it was conclusively demonstrated that Nabo can predict the identity of human HSPC subpopulations to the same accuracy as can be achieved by established cell-surface markers. Having Nabo at hand, we aimed to uncover the heterogeneity of hematopoietic cells from different stages of AML. Nabo showed that AML cells lacked the heterogeneity of normal CD34+ cells and were devoid of cells with HSC gene signature. A large patient-to-patient variability was found where leukemic cells mapped to distinct stages of myeloid progenitors. To ask whether this variability could reflect differences in leukemia-initiating cell identity, we induced leukemia in murine granulocyte-monocyte-lymphoid progenitors (GMLPs) using an inducible model for MLL-ENL-driven AML. On projection, more than 70% of MLL-ENL-activated cells mapped to a distinct Flt3+ subpopulation present within healthy GMLPs. Statistical validity of this projection was verified using two novel null models for testing cell projections: 1) ablated node model, wherein the mapping strength of target cells are evaluated after removal of high mapping score source nodes, and 2) high entropy features model, which rules out the background noise effect. By separating Flt3+ and Flt3- cells prior to activation of the fusion gene and performing in vitro replating assays, we could demonstrate that Flt3+ GMLPs contained 3-4 fold more leukemia-initiating cells (1/1.34 cells) than Flt3- GMLPs (1/4.89 cells), indicating that leukemia-initiating cells within GMLPs express Flt3. Taken together, Nabo represents a robust cell projection strategy for relevant analysis of scRNA-Seq data that permits an interpretable inference of cross-population relationships. Nabo is designed to compare disparate cellular populations by using the heterogeneity of one population as a point of reference allowing for cell-type specification even following perturbations that have resulted in large molecular changes to the cells of interest. As such, Nabo has critical implementation for delineation of leukemia heterogeneity and identification of leukemia-initiating cell population. Disclosures No relevant conflicts of interest to declare.
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Heuck, Christoph, Jayesh Mehta, Joseph Tariman, Natalie Pulliam, Yiting Yu, Tushar Bhagat, Sangeeta Nischal, et al. "Epigenomic Profiling of Multiple Myeloma Shows Widespread Stage Specific Alterations In DNA Methylation That Occur Early During Myelomagenesis." Blood 116, no. 21 (November 19, 2010): 784. http://dx.doi.org/10.1182/blood.v116.21.784.784.
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Abstract Abstract 784 Gene expression is a tightly regulated process and is influenced by aberrant epigenetic changes that can lead to carcinogenesis. We used the HELP (HpaII tiny fragment Enrichment by Ligation-mediated PCR) assay to perform an unbiased genome-wide analysis of DNA methylation in 11 MGUS, 16 newly diagnosed myeloma, 17 relapsed myeloma, and 8 healthy control samples. The HELP assay uses differential methylation specific restriction digestion by HpaII and MspI followed by amplification, 2 color labeling, and co-hybridization to quantitatively determine individual promoter methylation of 25,626 loci. The methylome analysis was performed using CD 138+ sorted bone marrow plasma cells in all cases. We observed extensive DNA methylation changes in myeloma that were seen even in MGUS cases when compared to normal plasma cells. Unsupervised clustering of all samples showed that MGUS samples were distinct but epigenetically closer to normal plasma cells than to cases of newly diagnosed or relapsed Myeloma. MGUS cases were characterized predominantly by aberrant hypomethylation, with 456 hypomethylated probes versus 130 hypermethylated probes (cutoff criteria were defined as a difference of means > 1.5 and significance of this difference with p < 0.001, Figure 1) that affected pathways regulated by NF-kb transcription factor. Untreated newly diagnosed myeloma samples were also predominantly hypomethylated (461 hypomethylated probes, 83 hypermethylated probes) when compared to controls. In addition to NF-kB, the MAP kinase and PI3 kinase regulated pathways were affected by hypomethylated genes. In contrast, cases of relapsed myeloma showed predominantly hypermethylated loci (221 hypomethylated, 560 hypermethylated probes) when compared to controls and involved the TNF and retinoblastoma pathways. A large number of important genes including the tumor suppressors CDKN2A and CDKN2B were aberrantly hypermethylated in this cohort. Figure 1: Volcano plot of differentially methylated genes, plotting difference of means against the −log(10) of the respective p-value. The cutoff for the definition for significant differentially methylated probes was set at a difference of means >1.5 or < −1.5 and a p-value < 0.001. Hypomethylated probes are depicted in green, hypermethylated probes in red. The respective numbers are documented in the same color. A) comparison of normal plasma cells with MGUS. B) comparison of normal plasma cells with newly diagnosed MM. C) comparison of normal plasma cell with relapsed myeloma Figure 1:. Volcano plot of differentially methylated genes, plotting difference of means against the −log(10) of the respective p-value. The cutoff for the definition for significant differentially methylated probes was set at a difference of means >1.5 or < −1.5 and a p-value < 0.001. Hypomethylated probes are depicted in green, hypermethylated probes in red. The respective numbers are documented in the same color. A) comparison of normal plasma cells with MGUS. B) comparison of normal plasma cells with newly diagnosed MM. C) comparison of normal plasma cell with relapsed myeloma Analysis of our differentially methylated targets using the Molecular Signatures Database (MSigDB, Tamayo, et al. 2005, PNAS 102, 15545–15550), showed significant overlap for hypomethylated genes in MGUS and new multiple myeloma (MM). These gene sets contain genes with promoter regions around transcription start site containing motifs which match annotation for transcription factors SP1 and TCF3 as well as enrichment for genes with gene ontology annotations for plasma membrane proteins. Hypermethylated genes in new MM show significant overlap with genes in the neighborhood of RAD23A, CTBP1, G22P1 and SMC1, suggesting impairment of DNA repair, as well as enrichment for genes associated with apoptosis and programmed cell death. In conclusion, genome-wide DNA methylation analysis is able to clearly differentiate between normal bone marrow plasma cells, MGUS as well as new MM and relapsed MM cells. Correlation of significantly differentially methylated genes with published gene sets reveals enrichment for genes involved in DNA repair, cell-cell signaling, cell death, apoptosis and cell cycle regulation. Disclosures: Mehta: Celgene: Consultancy, Speakers Bureau; Takeda/Millennium: Speakers Bureau; Onyx: Research Funding. Singhal:Takeda/Millennium: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Onyx: Research Funding; Celgene: Speakers Bureau.
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Niavarani, Ahmadreza, Tobias Herold, Yasmin Reyal, Erin Currie, Stuart Horswell, Arash Jalali, Maria Cristina Sauerland, et al. "A 16-Gene Signature Associated with High Levels of Wilms Tumor-1 (WT1) Expression Is an Adverse Prognostic Factor in Acute Myeloid Leukemia." Blood 124, no. 21 (December 6, 2014): 1021. http://dx.doi.org/10.1182/blood.v124.21.1021.1021.
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Abstract Wilms Tumor-1 (WT1) expression level has long been found to be implicated in acute myeloid leukemia (AML) prognosis, though this is not reflected in current AML risk stratification. We hypothesized that a gene expression profile (GEP) associated with WT1expression could be of prognostic value. We analyzed two publically available AML GEP series in order to identify a gene signature associated with high-WT1 expression (hi-WT1). The first, herein called Netherlands series, comprised of 524 younger adult patients who have been treated according to sequential Dutch-Belgian Hemato-Oncology Cooperative Group and the Swiss Group for Clinical Cancer Research (HOVON/SAKK) AML-04, 04A, 29, 32, 42, and 43 protocols (GSE14468). The second series, herein called Germany series, consisted of 562 younger and older AML patients who were treated in the German AMLCG 1999 trial (GSE37642). We identified the hi-WT1 gene sets by comparing GEP among the highest and lowest quartiles of WT1 expression in both AML studies. About 62% of the probe sets in the Netherlands hi-WT1 set were found to be common with the Germany hi-WT1 set; 97% differed in the same direction. Moreover, a high degree of correlation of the fold differences was found among the two hi-WT1 sets (r2 = 0.81, p < 10-18), collectively suggesting a biological relevance for hi-WT1gene sets. In order to assess the prognostic implication of the hi-WT1 set, we used K-Nearest Neighborhood algorithm to generate various lists of hi-WT1 probe sets predicting event-free survival (EFS) as the favorable, and all others (dead, no remission, progressive disease/relapse) as the unfavorable events in the Netherlands series. Stepwise screening of the lists of 10 to 100 probe sets by Cox Regression identified a 16-gene subset of hi-WT1 set with distinct GEP and as the optimal predictor of overall survival (OS) and EFS in the Netherlands series. It comprised of GPR56, FAM30A, NGFRAP1, WBP5, LTK, PTP4A3, CD109, ZC3H12C, PYGB, CHIC1, HAVCR2, TMEM110, HAL, HDAC4, BLVRA, and P2RY2. In this series, the hi-WT1 cluster of patients showed lower 5y-probability of OS (10% vs 44%) and EFS (6% vs 38%) as compared to the remaining clusters. Accordingly, the hi-WT1 cluster showed shorter median OS (8.3 [CI 6.7-9.9] vs 31.3 [CI 17.1-45.5] months, P = 6 x 10-18) and EFS (4.9 [CI 3.2-6.5] vs 14.5 [CI 9.4-19.5] months, P = 3 x 10-16). Although the hi-WT1 cluster was associated with some of the cytogenetic and molecular aberrations including FLT3-ITD, it remained significant for both OS (P = 3 x 10-5) and EFS (P = 3 x 10-6) after adjustment for known AML risk factors. In order to validate our findings, we performed a supervised clustering of the Germany AML series using the 16-gene signature. The hi-WT1 cluster predicted both adverse OS and relapse-free survival (RFS) (Fig. 1), which remained statistically significant after adjustment for known AML risk factors (Table 1). The median OS was 7.1 (CI 5.7-8.5) months for the hi-WT1 cluster as compared to 20.1 (CI 15.2- 25.0) months for other cases (P = 2 x 10-13), and the median RFS was 5.8 (CI 4.8-6.8) vs 20.3 (CI 10.6-30.0) months, respectively (P = 2 x 10-11). Moreover, the rate of complete remission was significantly lower in hi-WT1 cluster as compared to other clusters (42% vs 61%, P = 2 x 10-5). The positive (PPV) and negative predictive value (NPV) of the marker for prediction of adverse OS were 90% and 34%, respectively. These values were found to be 88% and 38%, respectively, for prediction of adverse RFS. MetaCore analysis identified the Antigen Presentation by MHC-II as the most implicated biological pathway in hi-WT1sets, with many genes downregulated in the pathway. In brief, we identified a 16-gene signature associated with WT1 expression and demonstrated its adverse and independent prognostic impact in adult AML patients. These promising results should be validated in further trials and provide new clues to the molecular mechanisms underlying WT1regulation. Figure 1 Figure 1. Kaplan-Meier analysis of OS and RFS in the Germany series separated by WT1-related 16-gene signature. Table 1. Multivariate analysis of OS and RFS in the Germany AML series. Variable OS RFS P val HR 95% CI for HR P val HR 95% CI for HR Lower Upper Lower Upper WT1 signature .001 1.485 1.186 1.860 .000 1.834 1.326 2.536 Age .000 1.291 1.197 1.391 .001 1.198 1.081 1.327 ELN2 .000 2.395 1.743 3.291 .000 3.147 2.124 4.663 ELN3 .000 2.395 1.732 3.312 .000 2.153 1.405 3.299 ELN4 .000 3.306 2.376 4.599 .000 6.401 4.027 10.175 Disclosures No relevant conflicts of interest to declare.
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Yuan, Xiaocui, Huawei Chen, and Baoling Liu. "Point cloud clustering and outlier detection based on spatial neighbor connected region labeling." Measurement and Control, May 27, 2020, 002029402091986. http://dx.doi.org/10.1177/0020294020919869.
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Clustering analysis is one of the most important techniques in point cloud processing, such as registration, segmentation, and outlier detection. However, most of the existing clustering algorithms exhibit a low computational efficiency with the high demand for computational resources, especially for large data processing. Sometimes, clusters and outliers are inseparable, especially for those point clouds with outliers. Most of the cluster-based algorithms can well identify cluster outliers but sparse outliers. We develop a novel clustering method, called spatial neighborhood connected region labeling. The method defines spatial connectivity criterion, finds points connections based on the connectivity criterion among the k-nearest neighborhood region and classifies connected points to the same cluster. Our method can accurately and quickly classify datasets using only one parameter k. Comparing with K-means, hierarchical clustering and density-based spatial clustering of applications with noise methods, our method provides better accuracy using less computational time for data clustering. For applications in the outlier detection of the point cloud, our method can identify not only cluster outliers, but also sparse outliers. More accurate detection results are achieved compared to the state-of-art outlier detection methods, such as local outlier factor and density-based spatial clustering of applications with noise.
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Zuo, Wendi, and Xinmin Hou. "An improved probability propagation algorithm for density peak clustering based on natural nearest neighborhood." Array, July 2022, 100232. http://dx.doi.org/10.1016/j.array.2022.100232.
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Khozaei, Bahareh, and Mahdi Eftekhari. "Unsupervised Feature Selection Based on Spectral Clustering with Maximum Relevancy and Minimum Redundancy Approach." International Journal of Pattern Recognition and Artificial Intelligence, August 17, 2021, 2150031. http://dx.doi.org/10.1142/s0218001421500312.
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In this paper, two novel approaches for unsupervised feature selection are proposed based on the spectral clustering. In the first proposed method, spectral clustering is employed over the features and the center of clusters is selected as well as their nearest-neighbors. These features have a minimum similarity (redundancy) between themselves since they belong to different clusters. Next, samples of data sets are clustered employing spectral clustering so that to the samples of each cluster a specific pseudo-label is assigned. After that according to the obtained pseudo-labels, the information gain of the features is computed that secures the maximum relevancy. Finally, the intersection of the selected features in the two previous steps is determined that simultaneously guarantees both the maximum relevancy and minimum redundancy. Our second proposed approach is very similar to the first one whose only but significant difference with the first method is that it selects one feature from each cluster and sorts all the features in terms of their relevancy. Then, by appending the selected features to a sorted list and ignoring them for the next step, the algorithm continues with the remaining features until all the features to be appended into the sorted list. Both of our proposed methods are compared with state-of-the-art methods and the obtained results confirm the performance of our proposed approaches especially the second one.
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Yihong, Li, Wang Yunpeng, Li Tao, Lan Xiaolong, and Song Han. "GNN-DBSCAN: A new density-based algorithm using grid and the nearest neighbor." Journal of Intelligent & Fuzzy Systems, August 23, 2021, 1–13. http://dx.doi.org/10.3233/jifs-211922.
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DBSCAN (density-based spatial clustering of applications with noise) is one of the most widely used density-based clustering algorithms, which can find arbitrary shapes of clusters, determine the number of clusters, and identify noise samples automatically. However, the performance of DBSCAN is significantly limited as it is quite sensitive to the parameters of eps and MinPts. Eps represents the eps-neighborhood and MinPts stands for a minimum number of points. Additionally, a dataset with large variations in densities will probably trap the DBSCAN because its parameters are fixed. In order to overcome these limitations, we propose a new density-clustering algorithm called GNN-DBSCAN which uses an adaptive Grid to divide the dataset and defines local core samples by using the Nearest Neighbor. With the help of grid, the dataset space will be divided into a finite number of cells. After that, the nearest neighbor lying in every filled cell and adjacent filled cells are defined as the local core samples. Then, GNN-DBSCAN obtains global core samples by enhancing and screening local core samples. In this way, our algorithm can identify higher-quality core samples than DBSCAN. Lastly, give these global core samples and use dynamic radius based on k-nearest neighbors to cluster the datasets. Dynamic radius can overcome the problems of DBSCAN caused by its fixed parameter eps. Therefore, our method can perform better on dataset with large variations in densities. Experiments on synthetic and real-world datasets were conducted. The results indicate that the average Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), Adjusted Mutual Information (AMI) and V-measure of our proposed algorithm outperform the existing algorithm DBSCAN, DPC, ADBSCAN, and HDBSCAN.
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Jia, Junbo, and Luonan Chen. "Single-cell RNA sequencing data analysis based on non-uniform ε−neighborhood network." Bioinformatics, February 21, 2022. http://dx.doi.org/10.1093/bioinformatics/btac114.
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Abstract Motivation Single-cell RNA sequencing (scRNA-seq) technology provides the possibility to study cell heterogeneity and cell development on the resolution of individual cells. Arguably, three of the most important computational targets on scRNA-seq data analysis are data visualization, cell clustering and trajectory inference. Although a substantial number of algorithms have been developed, most of them do not treat the three targets in a systematic or consistent manner. Results In this article, we propose an efficient scRNA-seq analysis framework, which accomplishes the three targets consistently by non-uniform ε−neighborhood (NEN) network. First, a network is generated by our NEN method, which combines the advantages of both k-nearest neighbors (KNN) and ε−neighborhood (EN) to represent the manifold that data points reside in gene space. Then from such a network, we use its layout, its community and further its shortest path to achieve the purpose of scRNA-seq data visualization, clustering and trajectory inference. The results on both synthetic and real datasets indicate that our NEN method not only can visually provide the global topological structure of a dataset accurately compared with t-SNE (t-Distributed Stochastic Neighbor Embedding) and UMAP (Uniform Manifold Approximation and Projection), but also has superior performances on clustering and pseudotime ordering of cells over the existing approaches. Availability and implementation This analysis method has been made into a python package called ccnet and is freely available at https://github.com/Just-Jia/ccNet. Supplementary information Supplementary data are available at Bioinformatics online.
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Yuan, Xiaocui, Baoling Liu, and Yongli Ma. "Anisotropic neighborhood searching for point cloud with sharp feature." Measurement and Control, October 20, 2020, 002029402096424. http://dx.doi.org/10.1177/0020294020964245.
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The k-nearest neighborhoods (kNN) of feature points of complex surface model are usually isotropic, which may lead to sharp feature blurring during data processing, such as noise removal and surface reconstruction. To address this issue, a new method was proposed to search the anisotropic neighborhood for point cloud with sharp feature. Constructing KD tree and calculating kNN for point cloud data, the principal component analysis method was employed to detect feature points and estimate normal vectors of points. Moreover, improved bilateral normal filter was used to refine the normal vector of feature point to obtain more accurate normal vector. The isotropic kNN of feature point were segmented by mapping the kNN into Gaussian sphere to form different data-clusters, with the hierarchical clustering method used to separate the data in Gaussian sphere into different clusters. The optimal anisotropic neighborhoods of feature point corresponded to the cluster data with the maximum point number. To validate the effectiveness of our method, the anisotropic neighbors are applied to point data processing, such as normal estimation and point cloud denoising. Experimental results demonstrate that the proposed algorithm in the work is more time-consuming, but provides a more accurate result for point cloud processing by comparing with other kNN searching methods. The anisotropic neighborhood searched by our method can be used to normal estimation, denoising, surface fitting and reconstruction et al. for point cloud with sharp feature, and our method can provide more accurate result comparing with isotropic neighborhood.
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"Failure Node Detection and Recovery in Wireless Sensor Networks." International Journal of Innovative Technology and Exploring Engineering 8, no. 12 (October 10, 2019): 5226–30. http://dx.doi.org/10.35940/ijitee.l2793.1081219.
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A Wireless Sensor Network may often consist of hundreds of distributed sensors. Our goal is to formulate wireless sensor networks (WSNs) fault identification problem in terms of pattern classification and to introduce a newly developed algorithm, neighbor node hidden conditional algorithm (NHCA) to determine the unknown path through which packets are transmitted from source to destination. We propose a concept of fault recovery in WSN using clustering. This includes the protocols of Dynamic Delegation based Efficient Broadcast and Neighborhood Hidden Conditional Random Field Algorithm. The history of transmission is classified according to the pattern, sorted and ranked along with the cluster information. The data privacy is maintained with in the cluster during the packet transmission apart from the destination which may present outside the cluster. The leader of the cluster is restricted only to view the transmission path in order to maintain the confidentiality of the data transmitted. Our simulation results strongly enforce fault recovery in quick time and also maintain the confidentiality of data.
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France, Michael T., Bing Ma, Pawel Gajer, Sarah Brown, Michael S. Humphrys, Johanna B. Holm, L. Elaine Waetjen, Rebecca M. Brotman, and Jacques Ravel. "VALENCIA: a nearest centroid classification method for vaginal microbial communities based on composition." Microbiome 8, no. 1 (November 23, 2020). http://dx.doi.org/10.1186/s40168-020-00934-6.
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Abstract Background Taxonomic profiles of vaginal microbial communities can be sorted into a discrete number of categories termed community state types (CSTs). This approach is advantageous because collapsing a hyper-dimensional taxonomic profile into a single categorical variable enables efforts such as data exploration, epidemiological studies, and statistical modeling. Vaginal communities are typically assigned to CSTs based on the results of hierarchical clustering of the pairwise distances between samples. However, this approach is problematic because it complicates between-study comparisons and because the results are entirely dependent on the particular set of samples that were analyzed. We sought to standardize and advance the assignment of samples to CSTs. Results We developed VALENCIA (VAginaL community state typE Nearest CentroId clAssifier), a nearest centroid-based tool which classifies samples based on their similarity to a set of reference centroids. The references were defined using a comprehensive set of 13,160 taxonomic profiles from 1975 women in the USA. This large dataset allowed us to comprehensively identify, define, and characterize vaginal CSTs common to reproductive age women and expand upon the CSTs that had been defined in previous studies. We validated the broad applicability of VALENCIA for the classification of vaginal microbial communities by using it to classify three test datasets which included reproductive age eastern and southern African women, adolescent girls, and a racially/ethnically and geographically diverse sample of postmenopausal women. VALENCIA performed well on all three datasets despite the substantial variations in sequencing strategies and bioinformatics pipelines, indicating its broad application to vaginal microbiota. We further describe the relationships between community characteristics (vaginal pH, Nugent score) and participant demographics (race, age) and the CSTs defined by VALENCIA. Conclusion VALENCIA provides a much-needed solution for the robust and reproducible assignment of vaginal community state types. This will allow unbiased analysis of both small and large vaginal microbiota datasets, comparisons between datasets and meta-analyses that combine multiple datasets.
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Xu, Yajin, Qiong Luo, and Hong Shu. "Optimal Excess Commuting Evaluation Based on Local Minimal Costs." Transportation Research Record: Journal of the Transportation Research Board, August 27, 2021, 036119812110315. http://dx.doi.org/10.1177/03611981211031530.
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Excess commuting refers to the value of unnecessary commuting or distance costs. Traditional commuting distance models adapt the most efficient scenario with people working in the nearest workplace geographically. Even though there have been some attempts to include constraints with commuter attributes and neighborhood features, problems arise with traditional geographical space and the subjectivity of these predefined characteristics. In this paper, we propose a method to calculate theoretical local minimal costs, which considers preferences that are inherently behavioral based on current work–home trips in the process of reassigning the work–home configuration. Our method is based on a feature space with a higher dimension and with the enlargement of attributes and relations of and between commuters and neighborhoods. Additionally, our solution is arrived at innovatively by improved Fuzzy C-Means clustering and linear programming. Unlike traditional clustering algorithms, our improved method adapts entropy information and selects the initial parameters based on the actual data rather than on prior knowledge. Using the real origin–destination matrix, theoretical minimal costs are calculated within each cluster, referred to as local minimal costs, and the average sum of local minimal costs is our theoretical minimal cost. The difference between the expected minimal cost and the actual cost is the excess commuting. Using our method, experimental results show that only 13% of the daily commuting distance in Wuhan could be avoided, and the theoretical distance is approximately 1.06 km shorter than the actual commuting distance.
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Kaya Keles, Mumine, Umit Kilic, and Abdullah Emre Keles. "Proposed Artificial Bee Colony Algorithm as Feature Selector to Predict the Leadership Perception of Site Managers." Computer Journal, December 24, 2020. http://dx.doi.org/10.1093/comjnl/bxaa163.
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Abstract Datasets have relevant and irrelevant features whose evaluations are fundamental for classification or clustering processes. The effects of these relevant features make classification accuracy more accurate and stable. At this point, optimization methods are used for feature selection process. This process is a feature reduction process finding the most relevant feature subset without decrement of the accuracy rate obtained by original feature sets. Varied nature inspiration-based optimization algorithms have been proposed as feature selector. The density of data in construction projects and the inability of extracting these data cause various losses in field studies. In this respect, the behaviors of leaders are important in the selection and efficient use of these data. The objective of this study is implementing Artificial Bee Colony (ABC) algorithm as a feature selection method to predict the leadership perception of the construction employees. When Random Forest, Sequential Minimal Optimization and K-Nearest Neighborhood (KNN) are used as classifier, 84.1584% as highest accuracy result and 0.805 as highest F-Measure result were obtained by using KNN and Random Forest classifier with proposed ABC Algorithm as feature selector. The results show that a nature inspiration-based optimization algorithm like ABC algorithm as feature selector is satisfactory in prediction of the Construction Employee’s Leadership Perception.
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"Triplet Contents based Medical Image Retrieval System for Lung Nodules CT Images Retrieval and Recognition Application." International Journal of Engineering and Advanced Technology 8, no. 6 (August 30, 2019): 3132–43. http://dx.doi.org/10.35940/ijeat.f9204.088619.
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Content Based Medical Image Retrieval (CBMIR) has found its relevance in medical diagnosis by processing massive medical databases based on visual and semantic features and user preferences. In this paper we address two issues such as retrieval and recognition. We present a novel method called Triplet-CBMIR for lung nodules CT images retrieval and recognition application. A Triplet CBMIR is a combination of three properties: Visual Features (Shape and Texture), Semantic Features and Relevance Feedback. Dataset training is done using: Preprocessing, Feature Extraction, Selection, Nodules Sign Detection and Clustering. In preprocessing we perform image scaling, denoising and normalization. In feature extraction, two methods are presented such as Hybrid Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT), Bounding-Box based Convolutional Network (CNN) for visual and semantic features extraction. Then optimum set of feature vectors are selected using Mutual Information based Neighborhood Entropy (MINε). Based on selected features, lung nodule sign is detected using K-nearest Neighbor (KNN) algorithm in which Hassanat Distance used and similar images are grouped using Multi-Self organizing Map (SOM). For similarity measurement, d_1 distance metric is used. Benchmark dataset such as LISS and LIDC are used for the study. Performance matrices such as Average Precision Rate (APR), Average Retrieval Rate (ARR), Average Recognition Rate (ArR), Running Time found in the simulation results are compared with some other already present state-of-the-art works. The proposed method shows a significant improvement as compared to other existing methods.
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Airoldi, C., C. Magnani, F. Lazzarato, D. Mirabelli, S. Tunesi, and D. Ferrante. "Environmental asbestos exposure and clustering of malignant mesothelioma in community: a spatial analysis in a population-based case–control study." Environmental Health 20, no. 1 (September 15, 2021). http://dx.doi.org/10.1186/s12940-021-00790-3.
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Abstract Background Neighborhood exposure to asbestos increases the risk of developing malignant mesothelioma (MM) in residents who live near asbestos mines and asbestos product plants. The area of Casale Monferrato (Northwest Italy) was impacted by several sources of asbestos environmental pollution, due to the presence of the largest Italian asbestos cement (AC) plant. In the present study, we examined the spatial variation of MM risk in an area with high levels of asbestos pollution and secondly, and we explored the pattern of clustering. Methods A population-based case–control study conducted between 2001 and 2006 included 200 cases and 348 controls. Demographic and occupational data along with residential information were recorded. Bivariate Kernel density estimation was used to map spatial variation in disease risk while an adjusted logistic model was applied to estimate the impact of residential distance from the AC plant. Kulldorf test and Cuzick Edward test were then performed. Results One hundred ninety-six cases and 322 controls were included in the analyses. The contour plot of the cases to controls ratio showed a well-defined peak of MM incidence near the AC factory, and the risk decreased monotonically in all directions when large bandwidths were used. However, considering narrower smoothing parameters, several peaks of increased risk were reported. A constant trend of decreasing OR with increasing distance was observed, with estimates of 10.9 (95% CI 5.32–22.38) and 10.48 (95%CI 4.54–24.2) for 0–5 km and 5–10 km, respectively (reference > 15 km). Finally, a significant (p < 0.0001) excess of cases near the pollution source was identified and cases are spatially clustered relative to the controls until 13 nearest neighbors. Conclusions In this study, we found an increasing pattern of mesothelioma risk in the area around a big AC factory and we detected secondary clusters of cases due to local exposure points, possibly associated to the use of asbestos materials.