Статті в журналах: "DBSCAN Method"

1

Chen, Guangsheng, Yiqun Cheng, and Weipeng Jing. "DBSCAN-PSM: an improvement method of DBSCAN algorithm on Spark." International Journal of High Performance Computing and Networking 13, no. 4 (2019): 417. http://dx.doi.org/10.1504/ijhpcn.2019.099265.

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Jing, Weipeng, Guangsheng Chen, and Yiqun Cheng. "DBSCAN-PSM: an improvement method of DBSCAN algorithm on Spark." International Journal of High Performance Computing and Networking 13, no. 4 (2019): 417. http://dx.doi.org/10.1504/ijhpcn.2019.10020624.

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Dillon, Pitisit, Pakinee Aimmanee, Akihiko Wakai, Go Sato, Hoang Viet Hung, and Jessada Karnjana. "A Novel Recursive Non-Parametric DBSCAN Algorithm for 3D Data Analysis with an Application in Rockfall Detection." Journal of Disaster Research 16, no. 4 (June 1, 2021): 579–87. http://dx.doi.org/10.20965/jdr.2021.p0579.

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The density-based spatial clustering of applications with noise (DBSCAN) algorithm is a well-known algorithm for spatial-clustering data point clouds. It can be applied to many applications, such as crack detection, rockfall detection, and glacier movement detection. Traditional DBSCAN requires two predefined parameters. Suitable values of these parameters depend upon the distribution of the input point cloud. Therefore, estimating these parameters is challenging. This paper proposed a new version of DBSCAN that can automatically customize the parameters. The proposed method consists of two processes: initial parameter estimation based on grid analysis and DBSCAN based on the divide-and-conquer (DC-DBSCAN) approach, which repeatedly performs DBSCAN on each cluster separately and recursively. To verify the proposed method, we applied it to a 3D point cloud dataset that was used to analyze rockfall events at the Puiggcercos cliff, Spain. The total number of data points used in this study was 15,567. The experimental results show that the proposed method is better than the traditional DBSCAN in terms of purity and NMI scores. The purity scores of the proposed method and the traditional DBSCAN method were 96.22% and 91.09%, respectively. The NMI scores of the proposed method and the traditional DBSCAN method are 0.78 and 0.49, respectively. Also, it can detect events that traditional DBSCAN cannot detect.

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Ma, Li, Lei Gu, Bo Li, Shouyi Qiao, and Jin Wang. "MRG-DBSCAN: An Improved DBSCAN Clustering Method Based on Map Reduce and Grid." International Journal of Database Theory and Application 8, no. 2 (April 30, 2015): 119–28. http://dx.doi.org/10.14257/ijdta.2015.8.2.12.

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Yu, Zhenhao, Fang Liu, Yinquan Yuan, Sihan Li, and Zhengying Li. "Signal Processing for Time Domain Wavelengths of Ultra-Weak FBGs Array in Perimeter Security Monitoring Based on Spark Streaming." Sensors 18, no. 9 (September 4, 2018): 2937. http://dx.doi.org/10.3390/s18092937.

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To detect perimeter intrusion accurately and quickly, a stream computing technology was used to improve real-time data processing in perimeter intrusion detection systems. Based on the traditional density-based spatial clustering of applications with noise (T-DBSCAN) algorithm, which depends on manual adjustments of neighborhood parameters, an adaptive parameters DBSCAN (AP-DBSCAN) method that can achieve unsupervised calculations was proposed. The proposed AP-DBSCAN method was implemented on a Spark Streaming platform to deal with the problems of data stream collection and real-time analysis, as well as judging and identifying the different types of intrusion. A number of sensing and processing experiments were finished and the experimental data indicated that the proposed AP-DBSCAN method on the Spark Streaming platform exhibited a fine calibration capacity for the adaptive parameters and the same accuracy as the T-DBSCAN method without the artificial setting of neighborhood parameters, in addition to achieving good performances in the perimeter intrusion detection systems.

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Nguyen, Trang T. D., Loan T. T. Nguyen, Anh Nguyen, Unil Yun, and Bay Vo. "A method for efficient clustering of spatial data in network space." Journal of Intelligent & Fuzzy Systems 40, no. 6 (June 21, 2021): 11653–70. http://dx.doi.org/10.3233/jifs-202806.

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Spatial clustering is one of the main techniques for spatial data mining and spatial data analysis. However, existing spatial clustering methods primarily focus on points distributed in planar space with the Euclidean distance measurement. Recently, NS-DBSCAN has been developed to perform clustering of spatial point events in Network Space based on a well-known clustering algorithm, named Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The NS-DBSCAN algorithm has efficiently solved the problem of clustering network constrained spatial points. When compared to the NC_DT (Network-Constraint Delaunay Triangulation) clustering algorithm, the NS-DBSCAN algorithm efficiently solves the problem of clustering network constrained spatial points by visualizing the intrinsic clustering structure of spatial data by constructing density ordering charts. However, the main drawback of this algorithm is when the data are processed, objects that are not specifically categorized into types of clusters cannot be removed, which is undeniably a waste of time, particularly when the dataset is large. In an attempt to have this algorithm work with great efficiency, we thus recommend removing edges that are longer than the threshold and eliminating low-density points from the density ordering table when forming clusters and also take other effective techniques into consideration. In this paper, we develop a theorem to determine the maximum length of an edge in a road segment. Based on this theorem, an algorithm is proposed to greatly improve the performance of the density-based clustering algorithm in network space (NS-DBSCAN). Experiments using our proposed algorithm carried out in collaboration with Ho Chi Minh City, Vietnam yield the same results but shows an advantage of it over NS-DBSCAN in execution time.

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Li, J. W., X. Q. Han, J. W. Jiang, Y. Hu, and L. Liu. "AN EFFICIENT CLUSTERING METHOD FOR DBSCAN GEOGRAPHIC SPATIO-TEMPORAL LARGE DATA WITH IMPROVED PARAMETER OPTIMIZATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 581–84. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-581-2020.

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Abstract. How to establish an effective method of large data analysis of geographic space-time and quickly and accurately find the hidden value behind geographic information has become a current research focus. Researchers have found that clustering analysis methods in data mining field can well mine knowledge and information hidden in complex and massive spatio-temporal data, and density-based clustering is one of the most important clustering methods.However, the traditional DBSCAN clustering algorithm has some drawbacks which are difficult to overcome in parameter selection. For example, the two important parameters of Eps neighborhood and MinPts density need to be set artificially. If the clustering results are reasonable, the more suitable parameters can not be selected according to the guiding principles of parameter setting of traditional DBSCAN clustering algorithm. It can not produce accurate clustering results.To solve the problem of misclassification and density sparsity caused by unreasonable parameter selection in DBSCAN clustering algorithm. In this paper, a DBSCAN-based data efficient density clustering method with improved parameter optimization is proposed. Its evaluation index function (Optimal Distance) is obtained by cycling k-clustering in turn, and the optimal solution is selected. The optimal k-value in k-clustering is used to cluster samples. Through mathematical and physical analysis, we can determine the appropriate parameters of Eps and MinPts. Finally, we can get clustering results by DBSCAN clustering. Experiments show that this method can select parameters reasonably for DBSCAN clustering, which proves the superiority of the method described in this paper.

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Elbatta, Mohammad T., Raed M. Bolbol, and Wesam M. Ashour. "A Vibration Method for Discovering Density Varied Clusters." ISRN Artificial Intelligence 2012 (November 15, 2012): 1–8. http://dx.doi.org/10.5402/2012/723516.

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DBSCAN is a base algorithm for density-based clustering. It can find out the clusters of different shapes and sizes from a large amount of data, which is containing noise and outliers. However, it is fail to handle the local density variation that exists within the cluster. Thus, a good clustering method should allow a significant density variation within the cluster because, if we go for homogeneous clustering, a large number of smaller unimportant clusters may be generated. In this paper, an enhancement of DBSCAN algorithm is proposed, which detects the clusters of different shapes and sizes that differ in local density. Our proposed method VMDBSCAN first finds out the “core” of each cluster—clusters generated after applying DBSCAN. Then, it “vibrates” points toward the cluster that has the maximum influence on these points. Therefore, our proposed method can find the correct number of clusters.

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Chen, Hao-xuan, Fei Tao, Pei-long Ma, Li-na Gao, and Tong Zhou. "Applicability Evaluation of Several Spatial Clustering Methods in Spatiotemporal Data Mining of Floating Car Trajectory." ISPRS International Journal of Geo-Information 10, no. 3 (March 12, 2021): 161. http://dx.doi.org/10.3390/ijgi10030161.

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Spatial analysis is an important means of mining floating car trajectory information, and clustering method and density analysis are common methods among them. The choice of the clustering method affects the accuracy and time efficiency of the analysis results. Therefore, clarifying the principles and characteristics of each method is the primary prerequisite for problem solving. Taking four representative spatial analysis methods—KMeans, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Clustering by Fast Search and Find of Density Peaks (CFSFDP), and Kernel Density Estimation (KDE)—as examples, combined with the hotspot spatiotemporal mining problem of taxi trajectory, through quantitative analysis and experimental verification, it is found that DBSCAN and KDE algorithms have strong hotspot discovery capabilities, but the heat regions’ shape of DBSCAN is found to be relatively more robust. DBSCAN and CFSFDP can achieve high spatial accuracy in calculating the entrance and exit position of a Point of Interest (POI). KDE and DBSCAN are more suitable for the classification of heat index. When the dataset scale is similar, KMeans has the highest operating efficiency, while CFSFDP and KDE are inferior. This paper resolves to a certain extent the lack of scientific basis for selecting spatial analysis methods in current research. The conclusions drawn in this paper can provide technical support and act as a reference for the selection of methods to solve the taxi trajectory mining problem.

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Zhao, Jianghong, Yan Dong, Siyu Ma, Huajun Liu, Shuangfeng Wei, Ruiju Zhang, and Xi Chen. "An Automatic Density Clustering Segmentation Method for Laser Scanning Point Cloud Data of Buildings." Mathematical Problems in Engineering 2019 (July 7, 2019): 1–13. http://dx.doi.org/10.1155/2019/3026758.

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Segmentation is an important step in point cloud data feature extraction and three-dimensional modelling. Currently, it is also a challenging problem in point cloud processing. There are some disadvantages of the DBSCAN method, such as requiring the manual definition of parameters and low efficiency when it is used for large amounts of calculation. This paper proposes the AQ-DBSCAN algorithm, which is a density clustering segmentation method combined with Gaussian mapping. The algorithm improves upon the DBSCAN algorithm by solving the problem of automatic estimation of the parameter neighborhood radius. The improved algorithm can carry out density clustering processing quickly by reducing the amount of computation required.

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Biondi, Maurizio, Paola D’Alessandro, Walter De Simone, and Mattia Iannella. "DBSCAN and GIE, Two Density-Based “Grid-Free” Methods for Finding Areas of Endemism: A Case Study of Flea Beetles (Coleoptera, Chrysomelidae) in the Afrotropical Region." Insects 12, no. 12 (December 13, 2021): 1115. http://dx.doi.org/10.3390/insects12121115.

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Areas of endemism (AoEs) are a central area of research in biogeography. Different methods have been proposed for their identification in the literature. In this paper, a “grid-free” method based on the “Density-based spatial clustering of applications with noise” (DBSCAN) is here used for the first time to locate areas of endemism for species belonging to the beetle tribe Chrysomelidae, Galerucinae, Alticini in the Afrotropical Region. The DBSCAN is compared with the “Geographic Interpolation of Endemism” (GIE), another “grid-free” method based on a kernel density approach. DBSCAN and GIE both return largely overlapping results, detecting the same geographical locations for the AoEs, but with different delimitations, surfaces, and number of detected sinendemisms. The consensus maps obtained by GIE are in general less clearly delimited than the maps obtained by DBSCAN, but nevertheless allow us to evaluate the core of the AoEs more precisely, representing of the percentage levels of the overlap of the centroids. DBSCAN, on the other hand, appears to be faster and more sensitive in identifying the AoEs. To facilitate implementing the delimitation of the AoEs through the procedure proposed by us, a new tool named “CLUENDA” (specifically developed is in GIS environment) is also made available.

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Yang, Kehua, Tian Tan, and Wei Zhang. "An Evidence Combination Method based on DBSCAN Clustering." Computers, Materials & Continua 57, no. 2 (2018): 269–81. http://dx.doi.org/10.32604/cmc.2018.03696.

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13

Scitovski, Rudolf, and Kristian Sabo. "DBSCAN-like clustering method for various data densities." Pattern Analysis and Applications 23, no. 2 (April 5, 2019): 541–54. http://dx.doi.org/10.1007/s10044-019-00809-z.

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Tkachenko, Anastasiia Yevhenivna, Liudmyla Olehivna Kyrychenko, and Tamara Anatoliivna Radyvylova. "Clustering Noisy Time Series." System technologies 3, no. 122 (October 10, 2019): 133–39. http://dx.doi.org/10.34185/1562-9945-3-122-2019-15.

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One of the urgent tasks of machine learning is the problem of clustering objects. Clustering time series is used as an independent research technique, as well as part of more complex data mining methods, such as rule detection, classification, anomaly detection, etc.A comparative analysis of clustering noisy time series is carried out. The clustering sample contained time series of various types, among which there were atypical objects. Clustering was performed by k-means and DBSCAN methods using various distance functions for time series.A numerical experiment was conducted to investigate the application of the k-means and DBSCAN methods to model time series with additive white noise. The sample on which clustering was carried out consisted of m time series of various types: harmonic realizations, parabolic realizations, and “bursts”.The work was carried out clustering noisy time series of various types.DBSCAN and k-means methods with different distance functions were used. The best results were shown by the DBSCAN method with the Euclidean metric and the CID function.Analysis of the results of the clustering of time series allows determining the key differences between the methods: if you can determine the number of clusters and you do not need to separate atypical time series, the k-means method shows fairly good results; if there is no information on the number of clusters and there is a problem of isolating non-typical rows, it is advisable to use the DBSCAN method.

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Wang, Chunxiao, Min Ji, Jian Wang, Wei Wen, Ting Li, and Yong Sun. "An Improved DBSCAN Method for LiDAR Data Segmentation with Automatic Eps Estimation." Sensors 19, no. 1 (January 5, 2019): 172. http://dx.doi.org/10.3390/s19010172.

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Point cloud data segmentation, filtering, classification, and feature extraction are the main focus of point cloud data processing. DBSCAN (density-based spatial clustering of applications with noise) is capable of detecting arbitrary shapes of clusters in spaces of any dimension, and this method is very suitable for LiDAR (Light Detection and Ranging) data segmentation. The DBSCAN method needs at least two parameters: The minimum number of points minPts, and the searching radius ε. However, the parameter ε is often harder to determine, which hinders the application of the DBSCAN method in point cloud segmentation. Therefore, a segmentation algorithm based on DBSCAN is proposed with a novel automatic parameter ε estimation method—Estimation Method based on the average of k nearest neighbors’ maximum distance—with which parameter ε can be calculated on the intrinsic properties of the point cloud data. The method is based on the fitting curve of k and the mean maximum distance. The method was evaluated on different types of point cloud data: Airborne, and mobile point cloud data with and without color information. The results show that the accuracy values using ε estimated by the proposed method are 75%, 74%, and 71%, which are higher than those using parameters that are smaller or greater than the estimated one. The results demonstrate that the proposed algorithm can segment different types of LiDAR point clouds with higher accuracy in a robust manner. The algorithm can be applied to airborne and mobile LiDAR point cloud data processing systems, which can reduce manual work and improve the automation of data processing.

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Wang, Siyuan, Gang Wang, and Jiarui Zhang. "Data Analysis Method of Terrorist Attacks Based on AHP-DBSCAN Method." Journal of Physics: Conference Series 1168 (February 2019): 032029. http://dx.doi.org/10.1088/1742-6596/1168/3/032029.

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Fu, Hongping, Hao Li, Yanqi Dong, Fu Xu, and Feixiang Chen. "Segmenting Individual Tree from TLS Point Clouds Using Improved DBSCAN." Forests 13, no. 4 (April 2, 2022): 566. http://dx.doi.org/10.3390/f13040566.

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Terrestrial laser scanning (TLS) can provide accurate and detailed three-dimensional (3D) structure information of the forest understory. Segmenting individual trees from disordered, discrete, and high-density TLS point clouds is the premise for obtaining accurate individual tree structure parameters of forest understory, pest control and fine modeling. In this study, we propose a bottom-up method to segment individual trees from TLS forest data based on density-based spatial clustering of applications with noise (DBSCAN). In addition, we also improve the DBSCAN based on the distance distribution matrix (DDM) to automatically and adaptively determine the optimal cluster number and the corresponding input parameters. Firstly, the proposed method is based on the improved DBSCAN to detect the trunks and obtain the initial clustering results. Then, the Hough circle fitting method is used to modify the trunk detection results. Finally, individual tree segmentation is realized based on regional growth layer-by-layer clustering. In this paper, we use TLS multi-station scanning data from Chinese artificial forest and German mixed forest, and then evaluate the efficiency of the method from three aspects: overall segmentation, trunk detection and small tree segmentation. Furthermore, the proposed method is compared with three existing individual tree segmentation methods. The results show that the total recall, precision, and F1-score of the proposed method are 90.84%, 95.38% and 0.93, respectively. Compared with traditional DBSCAN, recall, accuracy and F1-score are increased by 6.96%, 4.14% and 0.06, respectively. The individual tree segmentation result of the proposed method is comparable to those of the existing methods, and the optimal parameters can be automatically extracted and the small trees under tall trees can be accurately segmented.

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Piorecký, Marek, Jan Štrobl, and Vladimír Krajča. "AUTOMATIC EEG CLASSIFICATION USING DENSITY BASED ALGORITHMS DBSCAN AND DENCLUE." Acta Polytechnica 59, no. 5 (November 1, 2019): 498–509. http://dx.doi.org/10.14311/ap.2019.59.0498.

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Electroencephalograph (EEG) is a commonly used method in neurological practice. Automatic classifiers (algorithms) highlight signal sections with interesting activity and assist an expert with record scoring. Algorithm K-means is one of the most commonly used methods for EEG inspection. In this paper, we propose/apply a method based on density-oriented algorithms DBSCAN and DENCLUE. DBSCAN and DENCLUE separate the nested clusters against K-means. All three algorithms were validated on a testing dataset and after that adapted for a real EEG records classification. 24 dimensions EEG feature space were classified into 5 classes (physiological, epileptic, EOG, electrode, and EMG artefact). Modified DBSCAN and DENCLUE create more than two homogeneous classes of the epileptic EEG data. The results offer an opportunity for the EEG scoring in clinical practice. The big advantage of the proposed algorithms is the high homogeneity of the epileptic class.

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Gershtein, Arkadiy, and Andrey Terekhov. "DBSCAN clustering method is applied to identify severe Traffic Accident (TA) hotpots on roads." Computer Tools in Education, no. 1 (March 28, 2021): 45–57. http://dx.doi.org/10.32603/2071-2340-2021-1-46-58.

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DBSCAN clustering method is applied to identify severe Traffic Accident (TA) hotpots on roads. The research examines severe TA, defined as those that led to human damage (injury or death), in the city of Newton, MA and in the entire state of Massachusetts, USA from 2013 to 2018. DBSCAN algorithm was also applied to network-constrained uniformly distributed over road network data to locate threshold in number of points per cluster so that all more populated clusters identified in real data can be treated as statistically significant. For DBSCAN algorithm two types of distance metrics, Euclidean and over Network, were compared. It is found that both distances are equivalent on scale of 10 meters, which justifies hybrid approach to clustering: using Network distance only to generate uniformly distributed points needed for Monte-Carlo simulations. All clustering can be performed using Euclidean distances which is much faster and more memory efficient. Subsequent years analysis demonstrates the extend that hotspots identified are stable and occur consecutively for several years and hence may possess predictive value.

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Jufriansah, Adi, Yudhiakto Pramudya, Azmi Khusnani, and Sabarudin Saputra. "Analysis of Earthquake Activity in Indonesia by Clustering Method." Journal of Physics: Theories and Applications 5, no. 2 (September 30, 2021): 92. http://dx.doi.org/10.20961/jphystheor-appl.v5i2.59133.

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Indonesia is an area where three large tectonic plates meet, namely the Indo-Australian, Eurasian and Pacific plates, so that Indonesia is included in the earthquake-prone category, with 11,660 earthquake vibrations identified in the Meteorology, Climatology and Geophysics Agency (BMKG) database in 2019 The purpose of this study is to develop a classification of the distribution of earthquakes in Indonesia in 2019 based on the values of magnitude, depth, and position. This research was conducted by using the clustering method based on the K-means algorithm and the DBSCAN algorithm as a comparison. The results of the clustering show that the earthquake data analysis using the K-Means algorithm is superior with a silhouette index value of 0.837, while the DBSCAN algorithm has a silhouette index value of 0.730.

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Wang, Tianfu, Chang Ren, Yun Luo, and Jing Tian. "NS-DBSCAN: A Density-Based Clustering Algorithm in Network Space." ISPRS International Journal of Geo-Information 8, no. 5 (May 8, 2019): 218. http://dx.doi.org/10.3390/ijgi8050218.

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Spatial clustering analysis is an important spatial data mining technique. It divides objects into clusters according to their similarities in both location and attribute aspects. It plays an essential role in density distribution identification, hot-spot detection, and trend discovery. Spatial clustering algorithms in the Euclidean space are relatively mature, while those in the network space are less well researched. This study aimed to present a well-known clustering algorithm, named density-based spatial clustering of applications with noise (DBSCAN), to network space and proposed a new clustering algorithm named network space DBSCAN (NS-DBSCAN). Basically, the NS-DBSCAN algorithm used a strategy similar to the DBSCAN algorithm. Furthermore, it provided a new technique for visualizing the density distribution and indicating the intrinsic clustering structure. Tested by the points of interest (POI) in Hanyang district, Wuhan, China, the NS-DBSCAN algorithm was able to accurately detect the high-density regions. The NS-DBSCAN algorithm was compared with the classical hierarchical clustering algorithm and the recently proposed density-based clustering algorithm with network-constraint Delaunay triangulation (NC_DT) in terms of their effectiveness. The hierarchical clustering algorithm was effective only when the cluster number was well specified, otherwise it might separate a natural cluster into several parts. The NC_DT method excessively gathered most objects into a huge cluster. Quantitative evaluation using four indicators, including the silhouette, the R-squared index, the Davis–Bouldin index, and the clustering scheme quality index, indicated that the NS-DBSCAN algorithm was superior to the hierarchical clustering and NC_DT algorithms.

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Jing, Weipeng, Chuanyu Zhao, and Chao Jiang. "An improvement method of DBSCAN algorithm on cloud computing." Procedia Computer Science 147 (2019): 596–604. http://dx.doi.org/10.1016/j.procs.2019.01.208.

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Aditya, Wisnu, Herman Tolle, and Timothy K. Shih. "DBSCAN for Hand Tracking and Gesture Recognition." Journal of Information Technology and Computer Science 5, no. 2 (July 29, 2020): 168. http://dx.doi.org/10.25126/jitecs.202052174.

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Hand segmentation and tracking are important issues for hand-gesture recognition. Using depth data, it can speed up the segmentation process because we can delete unnecessary data like the background of the image easily. In this research, we modify DBSCAN clustering algorithm to make it faster and suitable for our system. This method is used in both hand tracking and hand gesture recognition. The results show that our method performs well in this system. The proposed method can outperform the original DBSCAN and the other clustering method in terms of computational time.

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Sembiring Brahmana, Rahma Wati, Fahd Agodzo Mohammed, and Kankamol Chairuang. "Customer Segmentation Based on RFM Model Using K-Means, K-Medoids, and DBSCAN Methods." Lontar Komputer : Jurnal Ilmiah Teknologi Informasi 11, no. 1 (April 30, 2020): 32. http://dx.doi.org/10.24843/lkjiti.2020.v11.i01.p04.

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A problem that appears in marketing activities is how to identify potential customers. Marketing activities could identify their best customer through customer segmentation by applying the concept of Data Mining and Customer Relationship Management (CRM). This paper presents the Data Mining process by combining the RFM model with K-Means, K-Medoids, and DBSCAN algorithms. This paper analyzes 334,641 transaction data and converts them to 1661 Recency, Frequency, and Monetary (RFM) data lines to identify potential customers. The K-Means, K-Medoids, and DBSCAN algorithms are very sensitive for initializing the cluster center because it is done randomly. Clustering is done by using two to six clusters. The trial process in the K-Means and K-Medoids Method is done using random centroid values ??and at DBSCAN is done using random Epsilon and Min Points, so that a cluster group is obtained that produces potential customers. Cluster validation completes using the Davies-Bouldin Index and Silhouette Index methods. The result showed that K-Means had the best level of validity than K-Medoids and DBSCAN, where the Davies-Bouldin Index yield was 0,33009058, and the Silhouette Index yield was 0,912671056. The best number of clusters produced using the Davies Bouldin Index and Silhouette Index are 2 clusters, where each K-Means, K-Medoids, and DBSCAN algorithms provide the Dormant and Golden customer classes.

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Ijaz, Muhammad Fazal, Muhammad Attique, and Youngdoo Son. "Data-Driven Cervical Cancer Prediction Model with Outlier Detection and Over-Sampling Methods." Sensors 20, no. 10 (May 15, 2020): 2809. http://dx.doi.org/10.3390/s20102809.

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Globally, cervical cancer remains as the foremost prevailing cancer in females. Hence, it is necessary to distinguish the importance of risk factors of cervical cancer to classify potential patients. The present work proposes a cervical cancer prediction model (CCPM) that offers early prediction of cervical cancer using risk factors as inputs. The CCPM first removes outliers by using outlier detection methods such as density-based spatial clustering of applications with noise (DBSCAN) and isolation forest (iForest) and by increasing the number of cases in the dataset in a balanced way, for example, through synthetic minority over-sampling technique (SMOTE) and SMOTE with Tomek link (SMOTETomek). Finally, it employs random forest (RF) as a classifier. Thus, CCPM lies on four scenarios: (1) DBSCAN + SMOTETomek + RF, (2) DBSCAN + SMOTE+ RF, (3) iForest + SMOTETomek + RF, and (4) iForest + SMOTE + RF. A dataset of 858 potential patients was used to validate the performance of the proposed method. We found that combinations of iForest with SMOTE and iForest with SMOTETomek provided better performances than those of DBSCAN with SMOTE and DBSCAN with SMOTETomek. We also observed that RF performed the best among several popular machine learning classifiers. Furthermore, the proposed CCPM showed better accuracy than previously proposed methods for forecasting cervical cancer. In addition, a mobile application that can collect cervical cancer risk factors data and provides results from CCPM is developed for instant and proper action at the initial stage of cervical cancer.

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de Berg, Mark, Ade Gunawan, and Marcel Roeloffzen. "Faster DBSCAN and HDBSCAN in Low-Dimensional Euclidean Spaces." International Journal of Computational Geometry & Applications 29, no. 01 (March 2019): 21–47. http://dx.doi.org/10.1142/s0218195919400028.

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We present a new algorithm for the widely used density-based clustering method dbscan. For a set of [Formula: see text] points in [Formula: see text] our algorithm computes the dbscan-clustering in [Formula: see text] time, irrespective of the scale parameter [Formula: see text] (and assuming the second parameter MinPts is set to a fixed constant, as is the case in practice). Experiments show that the new algorithm is not only fast in theory, but that a slightly simplified version is competitive in practice and much less sensitive to the choice of [Formula: see text] than the original dbscan algorithm. We also present an [Formula: see text] randomized algorithm for hdbscan in the plane — hdbscan is a hierarchical version of dbscan introduced recently — and we show how to compute an approximate version of hdbscan in near-linear time in any fixed dimension.

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Creţulescu, Radu G., Daniel I. Morariu, Macarie Breazu, and Daniel Volovici. "DBSCAN Algorithm for Document Clustering." International Journal of Advanced Statistics and IT&C for Economics and Life Sciences 9, no. 1 (June 1, 2019): 58–66. http://dx.doi.org/10.2478/ijasitels-2019-0007.

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AbstractDocument clustering is a problem of automatically grouping similar document into categories based on some similarity metrics. Almost all available data, usually on the web, are unclassified so we need powerful clustering algorithms that work with these types of data. All common search engines return a list of pages relevant to the user query. This list needs to be generated fast and as correct as possible. For this type of problems, because the web pages are unclassified, we need powerful clustering algorithms. In this paper we present a clustering algorithm called DBSCAN – Density-Based Spatial Clustering of Applications with Noise – and its limitations on documents (or web pages) clustering. Documents are represented using the “bag-of-words” representation (word occurrence frequency). For this type o representation usually a lot of algorithms fail. In this paper we use Information Gain as feature selection method and evaluate the DBSCAN algorithm by its capacity to integrate in the clusters all the samples from the dataset.

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Yohannes, Ervin, Fitri Utaminingrum, and Timothy K. Shih. "Clustering of Human Hand on Depth Image using DBSCAN Method." Journal of Information Technology and Computer Science 4, no. 2 (September 30, 2019): 177. http://dx.doi.org/10.25126/jitecs.201942133.

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In recent years, depth images are popular research in imageprocessing, especially in clustering field. The depth image can captureby depth cameras such as Kinect, Intel Real Sense, Leap Motion, and etc.Many objects and methods can be implemented in clustering field andissues. One of popular object is human hand since has many functionsand important parts of human body for daily routines. Besides, theclustering method has been developed for any goal and even combinewith another method. One of clustering method is Density-Based SpatialClustering of Applications with Noise (DBSCAN) which automaticclustering method consists of minimum points and epsilon. Define theepsilon in DBSCAN is important thing since the result depends on those.We want to look for the best epsilon for clustering human hand in thedepth images. We selected the epsilon from 5 until 100 for getting thebest clustering results. Moreover, those epsilons will be testing in threedistance to get accurate results.

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Santoso, Herdiesel, and Aina Musdholifah. "Case Base Reasoning (CBR) and Density Based Spatial Clustering Application with Noise (DBSCAN)-based Indexing in Medical Expert Systems." Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika 5, no. 2 (December 29, 2019): 169–78. http://dx.doi.org/10.23917/khif.v5i2.8323.

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Case-based Reasoning (CBR) has been widely applied in the medical expert systems. CBR has computational time constraints if there are too many old cases on the case base. Cluster analysis can be used as an indexing method to speed up searching in the case retrieval process. This paper propose retrieval method using Density Based Spatial Clustering Application with Noise (DBSCAN) for indexing and cosine similarity for the relevant cluster searching process. Three medical test data, that are malnutrition disease data, heart disease data and thyroid disease data, are used to measure the performance of the proposed method. Comparative tests conducted between DBSCAN and Self-organizing maps (SOM) for the indexing method, as well as between Manhattan distance similarity, Euclidean distance similarity and Minkowski distance similarity for calculating the similarity of cases. The result of testing on malnutrition and heart disease data shows that CBR with cluster-indexing has better accuracy and shorter processing time than non-indexing CBR. In the case of thyroid disease, CBR with cluster-indexing has a better average retrieval time, but the accuracy of non-indexing CBR is better than cluster indexing CBR. Compared to SOM algorithm, DBSCAN algorithm produces better accuracy and faster process to perform clustering and retrieval. Meanwhile, of the three methods of similarity, the Minkowski distance method produces the highest accuracy at the threshold ≥ 90.

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Starczewski, Artur, Piotr Goetzen, and Meng Joo Er. "A New Method for Automatic Determining of the DBSCAN Parameters." Journal of Artificial Intelligence and Soft Computing Research 10, no. 3 (July 1, 2020): 209–21. http://dx.doi.org/10.2478/jaiscr-2020-0014.

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AbstractClustering is an attractive technique used in many fields in order to deal with large scale data. Many clustering algorithms have been proposed so far. The most popular algorithms include density-based approaches. These kinds of algorithms can identify clusters of arbitrary shapes in datasets. The most common of them is the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The original DBSCAN algorithm has been widely applied in various applications and has many different modifications. However, there is a fundamental issue of the right choice of its two input parameters, i.e the eps radius and the MinPts density threshold. The choice of these parameters is especially difficult when the density variation within clusters is significant. In this paper, a new method that determines the right values of the parameters for different kinds of clusters is proposed. This method uses detection of sharp distance increases generated by a function which computes a distance between each element of a dataset and its k-th nearest neighbor. Experimental results have been obtained for several different datasets and they confirm a very good performance of the newly proposed method.

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Pavlis, Michalis, Les Dolega, and Alex Singleton. "A Modified DBSCAN Clustering Method to Estimate Retail Center Extent." Geographical Analysis 50, no. 2 (September 21, 2017): 141–61. http://dx.doi.org/10.1111/gean.12138.

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Lai, Wenhao, Mengran Zhou, Feng Hu, Kai Bian, and Qi Song. "A New DBSCAN Parameters Determination Method Based on Improved MVO." IEEE Access 7 (2019): 104085–95. http://dx.doi.org/10.1109/access.2019.2931334.

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de Moura Ventorim, Igor, Diego Luchi, Alexandre Loureiros Rodrigues, and Flávio Miguel Varejão. "BIRCHSCAN: A sampling method for applying DBSCAN to large datasets." Expert Systems with Applications 184 (December 2021): 115518. http://dx.doi.org/10.1016/j.eswa.2021.115518.

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Zhao, Jiafei, Rongkun Jiang, Xuetian Wang, and Hongmin Gao. "Robust CFAR Detection for Multiple Targets in K-Distributed Sea Clutter Based on Machine Learning." Symmetry 11, no. 12 (December 5, 2019): 1482. http://dx.doi.org/10.3390/sym11121482.

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For K-distributed sea clutter, a constant false alarm rate (CFAR) is crucial as a desired property for automatic target detection in an unknown and non-stationary background. In multiple-target scenarios, the target masking effect reduces the detection performance of CFAR detectors evidently. A machine learning based processor, associating the artificial neural network (ANN) and a clustering algorithm of density-based spatial clustering of applications with noise (DBSCAN), namely, DBSCAN-CFAR, is proposed herein to address this issue. ANN is trained with a symmetrical structure to estimate the shape parameter of background clutter, whereas DBSCAN is devoted to excluding interference targets and sea spikes as outliers in the leading and lagging windows that are symmetrical about the cell under test (CUT). Simulation results verified that the ANN-based method provides the optimal parameter estimation results in the range of 0.1 to 30, which facilitates the control of actual false alarm probability. The effectiveness and robustness of DBSCAN-CFAR are also confirmed by the comparisons of conventional CFAR processors in different clutter conditions, comprised of varying target numbers, shape parameters, and false alarm probabilities. Although the proposed ANN-based DBSCAN-CFAR processor incurs more elapsed time, it achieves superior CFAR performance without a prior knowledge on the number and distribution of interference targets.

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Han, Hongqi, Yongsheng Yu, Lijun Wang, Xiaorui Zhai, Yaxin Ran, and Jingpeng Han. "Disambiguating USPTO inventor names with semantic fingerprinting and DBSCAN clustering." Electronic Library 37, no. 2 (April 1, 2019): 225–39. http://dx.doi.org/10.1108/el-12-2018-0232.

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PurposeThe aim of this study is to present a novel approach based on semantic fingerprinting and a clustering algorithm called density-based spatial clustering of applications with noise (DBSCAN), which can be used to convert investor records into 128-bit semantic fingerprints. Inventor disambiguation is a method used to discover a unique set of underlying inventors and map a set of patents to their corresponding inventors. Resolving the ambiguities between inventors is necessary to improve the quality of the patent database and to ensure accurate entity-level analysis. Most existing methods are based on machine learning and, while they often show good performance, this comes at the cost of time, computational power and storage space.Design/methodology/approachUsing DBSCAN, the meta and textual data in inventor records are converted into 128-bit semantic fingerprints. However, rather than using a string comparison or cosine similarity to calculate the distance between pair-wise fingerprint records, a binary number comparison function was used in DBSCAN. DBSCAN then clusters the inventor records based on this distance to disambiguate inventor names.FindingsExperiments conducted on the PatentsView campaign database of the United States Patent and Trademark Office show that this method disambiguates inventor names with recall greater than 99 per cent in less time and with substantially smaller storage requirement.Research limitations/implicationsA better semantic fingerprint algorithm and a better distance function may improve precision. Setting of different clustering parameters for each block or other clustering algorithms will be considered to improve the accuracy of the disambiguation results even further.Originality/valueCompared with the existing methods, the proposed method does not rely on feature selection and complex feature comparison computation. Most importantly, running time and storage requirements are drastically reduced.

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Cheng, Dayu, Guo Yue, Tao Pei, and Mingbo Wu. "Clustering Indoor Positioning Data Using E-DBSCAN." ISPRS International Journal of Geo-Information 10, no. 10 (October 2, 2021): 669. http://dx.doi.org/10.3390/ijgi10100669.

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Indoor positioning data reflects human mobility in indoor spaces. Revealing patterns of indoor trajectories may help us understand human indoor mobility. Clustering methods, which are based on the measurement of similarity between trajectories, are important tools for identifying those patterns. However, due to the specific characteristics of indoor trajectory data, it is difficult for clustering methods to measure the similarity between trajectories. These characteristics are manifested in two aspects. The first is that the nodes of trajectories may have clear semantic attributes; for example, in a shopping mall, the node of a trajectory may contain information such as the store type and visit duration time, which may imply a customer’s interest in certain brands. The semantic information can only be obtained when the position precision is sufficiently high so that the relationship between the customer and the store can be determined, which is difficult to realize for outdoor positioning, either using GPS or mobile base station, due to the relatively large positioning error. If the tendencies of customers are to be considered, the similarity of geometrical morphology does not reflect the real similarity between trajectories. The second characteristic is the complex spatial shapes of indoor trajectory caused by indoor environments, which include elements such as closed spaces, multiple obstacles and longitudinal extensions. To deal with these challenges caused by indoor trajectories, in this article we proposed a new method called E-DBSCAN, which extended DBSCAN to trajectory clustering of indoor positioning data. First, the indoor location data were transformed into a sequence of residence points with rich semantic information, such as the type of store customer visited, stay time and spatial location of store. Second, a Weighted Edit Distance algorithm was proposed to measure the similarity of the trajectories. Then, an experiment was conducted to verify the correctness of E-DBSCAN using five days of positioning data in a shopping mall, and five shopping behavior patterns were identified and potential explanations were proposed. In addition, a comparison was conducted among E-DBSCAN, the k-means and DBSCAN algorithms. The experimental results showed that the proposed method can discover customers’ behavioral pattern in indoor environments effectively.

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Kushwah, Arun Pratap Singh, Shailesh Jaloree, and Ramjeevan Singh Thakur. "Computational analysis of incremental clustering approaches for Large Data." International Journal of Computers and Communications 15 (May 28, 2021): 14–18. http://dx.doi.org/10.46300/91013.2021.15.3.

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Clustering is an approach of data mining, which helps us to find the underlying hidden structure in the dataset. K-means is a clustering method which usages distance functions to find the similarities or dissimilarities between the instances. DBSCAN is a clustering algorithm, which discovers the arbitrary shapes & sizes of clusters from huge volume of using spatial density method. These two approaches of clustering are the classical methods for efficient clustering but underperform when the data is updated frequently in the databases so, the incremental or gradual clustering approaches are always preferred in this environment. In this paper, an incremental approach for clustering is introduced using K-means and DBSCAN to handle the new datasets dynamically updated in the database in an interval.

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Qin, Xiaoyu, Kai Ming Ting, Ye Zhu, and Vincent CS Lee. "Nearest-Neighbour-Induced Isolation Similarity and Its Impact on Density-Based Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4755–62. http://dx.doi.org/10.1609/aaai.v33i01.33014755.

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A recent proposal of data dependent similarity called Isolation Kernel/Similarity has enabled SVM to produce better classification accuracy. We identify shortcomings of using a tree method to implement Isolation Similarity; and propose a nearest neighbour method instead. We formally prove the characteristic of Isolation Similarity with the use of the proposed method. The impact of Isolation Similarity on densitybased clustering is studied here. We show for the first time that the clustering performance of the classic density-based clustering algorithm DBSCAN can be significantly uplifted to surpass that of the recent density-peak clustering algorithm DP. This is achieved by simply replacing the distance measure with the proposed nearest-neighbour-induced Isolation Similarity in DBSCAN, leaving the rest of the procedure unchanged. A new type of clusters called mass-connected clusters is formally defined. We show that DBSCAN, which detects density-connected clusters, becomes one which detects mass-connected clusters, when the distance measure is replaced with the proposed similarity. We also provide the condition under which mass-connected clusters can be detected, while density-connected clusters cannot.

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Chen, Fang, Tao Zhang, and Ruilin Liu. "An Active Learning Method Based on Variational Autoencoder and DBSCAN Clustering." Computational Intelligence and Neuroscience 2021 (July 30, 2021): 1–11. http://dx.doi.org/10.1155/2021/9952596.

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Active learning is aimed to sample the most informative data from the unlabeled pool, and diverse clustering methods have been applied to it. However, the distance-based clustering methods usually cannot perform well in high dimensions and even begin to fail. In this paper, we propose a new active learning method combined with variational autoencoder (VAE) and density-based spatial clustering of applications with noise (DBSCAN). It overcomes the difficulty of distance representation in high dimensions and prevents the distance concentration phenomenon from occurring in the computational learning literature with respect to high-dimensional p-norms. Finally, we compare our method with four common active learning methods and two other clustering algorithms combined with VAE on three datasets. The results demonstrate that our approach achieves competitive performance, and it is a new batch mode active learning algorithm designed for neural networks with a relatively small query batch size.

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Chen, Runzi, Shuliang Zhao, and Meishe Liang. "A Fast Multiscale Clustering Approach Based on DBSCAN." Wireless Communications and Mobile Computing 2021 (July 28, 2021): 1–11. http://dx.doi.org/10.1155/2021/4071177.

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Multiscale brings great benefits for people to observe objects or problems from different perspectives. It has practical significance for clustering on multiscale data. At present, there is a lack of research on the clustering of large-scale data under the premise that clustering results of small-scale datasets have been obtained. If one does cluster on large-scale datasets by using traditional methods, two disadvantages are as follows: (1) Clustering results of small-scale datasets are not utilized. (2) Traditional method will cause more running overhead. Aims at these shortcomings, this paper proposes a multiscale clustering framework based on DBSCAN. This framework uses DBSCAN for clustering small-scale datasets, then introduces algorithm Scaling-Up Cluster Centers (SUCC) generating cluster centers of large-scale datasets by merging clustering results of small-scale datasets, not mining raw large-scale datasets. We show experimentally that, compared to traditional algorithm DBACAN and leading algorithms DBSCAN++ and HDBSCAN, SUCC can provide not only competitive performance but reduce computational cost. In addition, under the guidance of experts, the performance of SUCC is more competitive in accuracy.

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Choi, Changlock, and Seong-Yun Hong. "MDST-DBSCAN: A Density-Based Clustering Method for Multidimensional Spatiotemporal Data." ISPRS International Journal of Geo-Information 10, no. 6 (June 6, 2021): 391. http://dx.doi.org/10.3390/ijgi10060391.

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The increasing use of mobile devices and the growing popularity of location-based ser-vices have generated massive spatiotemporal data over the last several years. While it provides new opportunities to enhance our understanding of various urban dynamics, it poses challenges at the same time due to the complex structure and large-volume characteristic of the spatiotemporal data. To facilitate the process and analysis of such spatiotemporal data, various data mining and clustering methods have been proposed, but there still needs to develop a more flexible and computationally efficient method. The purpose of this paper is to present a clustering method that can work with large-scale, multidimensional spatiotemporal data in a reliable and efficient manner. The proposed method, called MDST-DBSCAN, is applied to idealized patterns and a real data set, and the results from both examples demonstrate that it can identify clusters accurately within a reasonable amount of time. MDST-DBSCAN performs well on both spatial and spatiotemporal data, and it can be particularly useful for exploring massive spatiotemporal data, such as detailed real estate transactions data in Seoul, Korea.

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Lee, Seung-Mok, Young-Hyung Kim, and Jae-Kwon Eem. "A Method of Edge Line Estimation for Panel Glass Images using DBSCAN Algorithm." Journal of Korean Institute of Information Technology 19, no. 5 (May 31, 2021): 81–86. http://dx.doi.org/10.14801/jkiit.2021.19.5.81.

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Fakhraddingizi, Aygul. "Modification of the DBSCAN algorithm for big data clustering." Problems of Information Technology 13, no. 1 (January 24, 2022): 28–37. http://dx.doi.org/10.25045/jpit.v13.i1.04.

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The development of Information and Communication Technologies (ICT) has led to the rapid growth of digital information and the consequent emergence of the concept of large-scale data. Therefore, there is a need to delve into large-scale data and its essence, the possibilities and problems of analytical technologies. Clustering is one of the main methods of analyzing big data. The main purpose of clustering is to separate data into clusters according to certain characteristics. When clusters come in different sizes, densities, and shapes, the problem of detection arises. The article explores the density-based DBSCAN clustering algorithm for working with big data. One of the main features of this algorithm is to create an effective cluster by detecting the noise points in big data. During the implementation of the algorithm, real databases containing noise points were used. Metrics such as adjusted rand index, homogeneity, Davis-Boldin index were used to evaluate the results of the experiment. The proposed method was more effective than the traditional DBSCAN algorithm in detecting noise points.

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Zhang, Hanqi, Xi Xiao, Shiguang Ni, Changsheng Dou, Wei Zhou, and Shutao Xia. "Smartwatch User Authentication by Sensing Tapping Rhythms and Using One-Class DBSCAN." Sensors 21, no. 7 (April 2, 2021): 2456. http://dx.doi.org/10.3390/s21072456.

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As important sensors in smart sensing systems, smartwatches are becoming more and more popular. Authentication can help protect the security and privacy of users. In addition to the classic authentication methods, behavioral factors can be used as robust measures for this purpose. This study proposes a lightweight authentication method for smartwatches based on edge computing, which identifies users by their tapping rhythms. Based on the DBSCAN clustering algorithm, a new classification method called One-Class DBSCAN is presented. It first seeks core objects and then leverages them to perform user authentication. We conducted extensive experiments on 6110 real data samples collected from more than 600 users. The results show that our method achieved the lowest Equal Error Rate (EER) of only 0.92%, which was lower than those of other state-of-the-art methods. In addition, a statistical method for detecting the security level of a tapping rhythm is proposed. It can prevent users from setting a simple tapping rhythm password, and thus improve the security of smartwatches.

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Yu, Hui, LuYuan Chen, JingTao Yao, and XingNan Wang. "A three-way clustering method based on an improved DBSCAN algorithm." Physica A: Statistical Mechanics and its Applications 535 (December 2019): 122289. http://dx.doi.org/10.1016/j.physa.2019.122289.

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Purwar, Archana, and Sandeep Kumar Singh. "DBSCANI: Noise-Resistant Method for Missing Value Imputation." Journal of Intelligent Systems 25, no. 3 (July 1, 2016): 431–40. http://dx.doi.org/10.1515/jisys-2014-0172.

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AbstractThe quality of data is an important task in the data mining. The validity of mining algorithms is reduced if data is not of good quality. The quality of data can be assessed in terms of missing values (MV) as well as noise present in the data set. Various imputation techniques have been studied in MV study, but little attention has been given on noise in earlier work. Moreover, to the best of knowledge, no one has used density-based spatial clustering of applications with noise (DBSCAN) clustering for MV imputation. This paper proposes a novel technique density-based imputation (DBSCANI) built on density-based clustering to deal with incomplete values in the presence of noise. Density-based clustering algorithm proposed by Kriegal groups the objects according to their density in spatial data bases. The high-density regions are known as clusters, and the low-density regions refer to the noise objects in the data set. A lot of experiments have been performed on the Iris data set from life science domain and Jain’s (2D) data set from shape data sets. The performance of the proposed method is evaluated using root mean square error (RMSE) as well as it is compared with existing K-means imputation (KMI). Results show that our method is more noise resistant than KMI on data sets used under study.

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Pakuani, Kamilia Wafa, and Robert Kurniawan. "Kajian Penentuan Nilai Epsilon Optimal Pada Algoritma DMDBSCAN Dan Pemetaan Daerah Rawan Gempa Bumi Di Indonesia Tahun 2014-2020." Seminar Nasional Official Statistics 2021, no. 1 (November 1, 2021): 991–1000. http://dx.doi.org/10.34123/semnasoffstat.v2021i1.847.

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Area pengawasan gempa bumi dapat dilakukan dengan menemukan penyebaran poin gempa atau pengelompokan gempa acak. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) adalah salah satu algoritma clustering yang disegel dari sejumlah data besar yang mengandung noise atau outlier. Penelitian sebelumnya mengubah Algoritma DBSCAN untuk secara otomatis menemukan nilai Epsilon (Eps) optimal dengan menggunakan metode Algoritma Dynamic Method DBSCAN (DMDBSCAN). Nilai parameter Eps diperoleh dari perhitungan perubahan slope atau kemiringan garis maksimum pada 3 jarak dari tetangga paling dekat dalam distribusi data. Namun, cara ini rentan terhadap perubahan kemiringan garis yang sangat jauh. Maka dari itu, penelitian ini melakukan modifikasi cara tersebut dengan mencari nilai minimal pada rentang slope antara 10% hingga 20%. Nilai Eps yang dihasilkan setelah modifikasi menunjukkan angka yang lebih baik. Oleh karena itu, cara ini diharapkan dapat menjadi referensi untuk pencarian parameter Eps dalam Algoritma DMDBSCAN yang lebih cocok dan mengetahui distribusi titik gempa di Indonesia.

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Liu, Hui, Yang Liu, Zhenquan Qin, Ran Zhang, Zheng Zhang, and Liao Mu. "A Novel DBSCAN Clustering Algorithm via Edge Computing-Based Deep Neural Network Model for Targeted Poverty Alleviation Big Data." Wireless Communications and Mobile Computing 2021 (June 26, 2021): 1–10. http://dx.doi.org/10.1155/2021/5536579.

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Big data technology has been developed rapidly in recent years. The performance improvement mechanism of targeted poverty alleviation is studied through the big data technology to further promote the comprehensive application of big data technology in poverty alleviation and development. Using the data mining knowledge to accurately identify the poor population under the framework of big data, compared with the traditional identification method, it is obviously more accurate and persuasive, which is also helpful to find out the real causes of poverty and assist the poor residents in the future. In the current targeted poverty alleviation work, the identification of poor households and the matching of assistance measures are mainly through the visiting of village cadres and the establishment of documents. Traditional methods are time-consuming, laborious, and difficult to manage. It always omits lots of useful family information. Therefore, new technologies need to be introduced to realize intelligent identification of poverty-stricken households and reduce labor costs. In this paper, we introduce a novel DBSCAN clustering algorithm via the edge computing-based deep neural network model for targeted poverty alleviation. First, we deploy an edge computing-based deep neural network model. Then, in this constructed model, we execute data mining for the poverty-stricken family. In this paper, the DBSCAN clustering algorithm is used to excavate the poverty features of the poor households and complete the intelligent identification of the poor households. In view of the current situation of high-dimensional and large-volume poverty alleviation data, the algorithm uses the relative density difference of grid to divide the data space into regions with different densities and adopts the DBSCAN algorithm to cluster the above result, which improves the accuracy of DBSCAN. This avoids the need for DBSCAN to traverse all data when searching for density connections. Finally, the proposed method is utilized for analyzing and mining the poverty alleviation data. The average accuracy is more than 96%. The average F -measure, NMI, and PRE values exceed 90%. The results show that it provides decision support for precise matching and intelligent pairing of village cadres in poverty alleviation work.

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Jiang, Haihang, Fei Yang, Xin Zhu, Zhenxing Yao, and Tao Zhou. "Improved F-DBSCAN for Trip End Identification Using Mobile Phone Data in Combination with Base Station Density." Journal of Advanced Transportation 2022 (April 30, 2022): 1–17. http://dx.doi.org/10.1155/2022/3099721.

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Trip end identification based on mobile phone data has been widely investigated in recent years. However, the existing studies generally use fixed clustering radii (CR) in trip end clustering algorithms, but ignore the influence of base station (BS) densities on the positioning accuracy of mobile phone data. This paper proposes a new two-step method for identifying trip ends: (1) Genetic Algorithm (GA) is utilized to optimize the CRs of DBSCAN under different BS densities. (2) We propose an improved Fast-DBSCAN (F-DBSCAN) for two objectives. One is for improving identification accuracies; the parameter CRs for judging core points can be dynamically adjusted based on the BS density around each mobile phone trace. The other is for reducing time complexity; a fast clustering improvement for the algorithm is proposed. Mobile phone data was collected by real-name volunteers with support from the communication operator. We compare the identification accuracy and time complexity of the proposed method with the existing ones. Results show that the accuracy is raised to 85%, which is approximately 6% higher than the existing methods. Meanwhile, the median running time can be reduced by about 76% by the fast clustering improvement. Especially for noncommuting trip ends, the identification accuracy can be increased by 8%. The average identification errors of travel time and trip end coordinates are reduced by about 12 min and 321 m, respectively.

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Zhou, Hui, Zesen Gui, Jiang Zhang, Qun Zhou, Xueshan Liu, and Xiaoyang Ma. "A Quantification Method for Supraharmonic Emissions Based on Outlier Detection Algorithms." Energies 14, no. 19 (October 7, 2021): 6404. http://dx.doi.org/10.3390/en14196404.

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Based on outlier detection algorithms, a feasible quantification method for supraharmonic emission signals is presented. It is designed to tackle the requirements of high-resolution and low data volume simultaneously in the frequency domain. The proposed method was developed from the skewed distribution data model and the self-tuning parameters of density-based spatial clustering of applications with noise (DBSCAN) algorithm. Specifically, the data distribution of the supraharmonic band was analyzed first by the Jarque–Bera test. The threshold was determined based on the distribution model to filter out noise. Subsequently, the DBSCAN clustering algorithm parameters were adjusted automatically, according to the k-dist curve slope variation and the dichotomy parameter seeking algorithm, followed by the clustering. The supraharmonic emission points were analyzed as outliers. Finally, simulated and experimental data were applied to verify the effectiveness of the proposed method. On the basis of the detection results, a spectrum with the same resolution as the original spectrum was obtained. The amount of data declined by more than three orders of magnitude compared to the original spectrum. The presented method will benefit the analysis of quantification for the amplitude and frequency of supraharmonic emissions.

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