Relevant bibliographies by topics / Sorted nearest neighborhood clustering

Academic literature on the topic 'Sorted nearest neighborhood clustering'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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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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Dissertations / Theses on the topic "Sorted nearest neighborhood clustering"

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Vatsalan, Dinusha. "Scalable and approximate privacy-preserving record linkage." Phd thesis, 2014. http://hdl.handle.net/1885/12370.

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Record linkage, the task of linking multiple databases with the aim to identify records that refer to the same entity, is occurring increasingly in many application areas. Generally, unique entity identifiers are not available in all the databases to be linked. Therefore, record linkage requires the use of personal identifying attributes, such as names and addresses, to identify matching records that need to be reconciled to the same entity. Often, it is not permissible to exchange personal identifying data across different organizations due to privacy and confidentiality concerns or regulations. This has led to the novel research area of privacy-preserving record linkage (PPRL). PPRL addresses the problem of how to link different databases to identify records that correspond to the same real-world entities, without revealing the identities of these entities or any private or confidential information to any party involved in the process, or to any external party, such as a researcher. The three key challenges that a PPRL solution in a real-world context needs to address are (1) scalability to largedatabases by efficiently conducting linkage; (2) achieving high quality of linkage through the use of approximate (string) matching and effective classification of the compared record pairs into matches (i.e. pairs of records that refer to the same entity) and non-matches (i.e. pairs of records that refer to different entities); and (3) provision of sufficient privacy guarantees such that the interested parties only learn the actual values of certain attributes of the records that were classified as matches, and the process is secure with regard to any internal or external adversary. In this thesis, we present extensive research in PPRL, where we have addressed several gaps and problems identified in existing PPRL approaches. First, we begin the thesis with a review of the literature and we propose a taxonomy of PPRL to characterize existing techniques. This allows us to identify gaps and research directions. In the remainder of the thesis, we address several of the identified shortcomings. One main shortcoming we address is a framework for empirical and comparative evaluation of different PPRL solutions, which has not been studied in the literature so far. Second, we propose several novel algorithms for scalable and approximate PPRL by addressing the three main challenges of PPRL. We propose efficient private blocking techniques, for both three-party and two-party scenarios, based on sorted neighborhood clustering to address the scalability challenge. Following, we propose two efficient two-party techniques for private matching and classification to address the linkage quality challenge in terms of approximate matching and effective classification. Privacy is addressed in these approaches using efficient data perturbation techniques including k-anonymous mapping, reference values, and Bloom filters. Finally, the thesis reports on an extensive comparative evaluation of our proposed solutions with several other state-of-the-art techniques on real-world datasets, which shows that our solutions outperform others in terms of all three key challenges.
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Book chapters on the topic "Sorted nearest neighborhood clustering"

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Vatsalan, Dinusha, and Peter Christen. "Sorted Nearest Neighborhood Clustering for Efficient Private Blocking." In Advances in Knowledge Discovery and Data Mining, 341–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37456-2_29.

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Guru, D. S., and H. S. Nagendraswamy. "Clustering of Interval-Valued Symbolic Patterns Based on Mutual Similarity Value and the Concept of k-Mutual Nearest Neighborhood." In Computer Vision – ACCV 2006, 234–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11612704_24.

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Sarwar, Badrul M., Joseph A. Konstan, and John T. Riedl. "Distributed Recommender Systems for Internet Commerce." In Encyclopedia of Information Science and Technology, First Edition, 907–11. IGI Global, 2005. http://dx.doi.org/10.4018/978-1-59140-553-5.ch159.

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Recommender systems (RSs) present an alternative information-evaluation approach based on the judgements of human beings (Resnick & Varian, 1997). It attempts to automate the word-of-mouth recommendations that we regularly receive from family, friends, and colleagues. In essence, it allows everyone to serve as a critic. This inclusiveness circumvents the scalability problems of individual critics—with millions of readers it becomes possible to review millions of books. At the same time it raises the question of how to reconcile the many and varied opinions of a large community of ordinary people. Recommender systems address this question through the use of different algorithms: nearest-neighbor algorithms (Resnick, Iacovou, Suchak, Bergstrom, & Riedl, 1994; Shardanand et al., 1994), item-based algorithms (Sarwar, Karypis, Konstan, & Riedl, 2001), clustering algorithms (Ungar & Foster, 1998), and probabilistic and rule-based learning algorithms (Breese, Heckerman, & Kadie, 1998), to name but a few. The nearest-neighbor-algorithm-based recommender systems, which are often referred to as collaborative filtering (CF) systems in research literature (Maltz & Ehrlich, 1995), are the most widely used recommender systems in practice. A typical CF-based recommender system maintains a database containing the ratings that each customer has given to each product that customer has evaluated. For each customer in the system, the recommendation engine computes a neighborhood of other customers with similar opinions. To evaluate other products for this customer, the system forms a normalized and weighted average of the opinions of the customer’s neighbors.
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"Exploratory Data Analysis." In Spatial Analysis Techniques Using MyGeoffice®, 112–36. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3270-5.ch006.

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Exploratory data analysis (EDA) tries to summarize datasets main characteristics such as nearest neighborhood indexes, standard deviation, scatterplots or quadrat analysis. This EDA chapter is divided into several sections to cover myGeoffice© options not forgetting the graphical mode when facing outputs: file data input (after all, any analysis demands data); Descriptive study of the variable (mean, kurtosis, distribution plot, etc.); 2D-3D data posting (spatial location of the data samples); Cutoff layout map (a spatial colorful plot according to the data samples values that are higher and lower against any particular threshold); G and Kipley's K Index (to disclose clustered, uniform and random space sampling); Kernel Gaussian density (a non-parametric way to estimate the probability space density function of a variable); T-Student and F-tests (a parametric approach to check statistical differences between two sub-regions), including a brief section regarding the two-way ANOVA technique; Quadrat analysis (comparison of the statistically expected and actual counts of objects within spatial sampling areas to test randomness and clustering); XX profile scatterplot (silhouette view of the data along XX axis); and YY profile scatterplot (silhouette view of the data along YY axis).
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Conference papers on the topic "Sorted nearest neighborhood clustering"

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Vatsalan, Dinusha, Peter Christen, and Vassilios S. Verykios. "Efficient two-party private blocking based on sorted nearest neighborhood clustering." In the 22nd ACM international conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2505515.2505757.

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Louhi, Ibrahim, Lydia Boudjeloud-Assala, and Thomas Tamisier. "Incremental nearest neighborhood graph for data stream clustering." In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727506.

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Gavagsaz, Elahe, Mahmoud Naghibzadeh, and Mehrdad Jalali. "Conceptual summarization using ontologies and nearest neighborhood clustering." In 2011 International Conference on Semantic Technology and Information Retrieval (STAIR). IEEE, 2011. http://dx.doi.org/10.1109/stair.2011.5995756.

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Zhang, Xianxia, Ye Jiang, Tao Zou, Chenkun Qi, and Guitao Cao. "Data-driven based 3-D fuzzy logic controller design using nearest neighborhood clustering and linear support vector regression." In 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2011. http://dx.doi.org/10.1109/fuzzy.2011.6007684.

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Aquil, Mohd Minhajuddin, and Mir Iqbal Faheem. "Comparative Study on Spatial Clustering Methods for Identifying Traffic Accident Hotspots." In International Web Conference in Civil Engineering for a Sustainable Planet. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.112.64.

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Traffic accidents in an urban road network are inevitable as a result claims and disputes arise among different road users. It is imperative to estimate the likelihood of traffic accidents resulting from different factors that contribute to loss of life, property and health of road users. There is a pressing need to reduce traffic accidents by identifying the location of accident hotspots using suitable analysis methods and examining them which is essential for the safety of road users. In this research traffic accident hotspots are identified using two spatial clustering analysis methods namely Getis-Ord Gi* and Nearest Neighborhood Hierarchy (NNH). These methods are compared and evaluated using the Prediction Accuracy Index (PAI) for their degree of accuracy. In this study, a cumulative traffic accident data of Hyderabad city of Telangana state over four years is researched upon and considered. Getis-Ord Gi* analysis measures the concentration ratio based on Z score identified as high (positive Z-values) and low values (negative Z-values). NNH analysis is another spatial clustering method which displays hotspot regions in the form of Convex hulls and Ellipses. The choice of the above two clustering methods represents the significance of the precision required. The findings of the study reveal that NNH method performed better compared to Getis-Ord Gi* method in its ability to detect hotspots. The above research methodology can be performed to any size of road network area globally having relevant accident data for the identification of hotspots for reducing the traffic accidents.
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