Results 11 to 20 of about 761,172 (262)
A Systematic Review of Density Grid-Based Clustering for Data Streams
IEEE Access, 2022 Various applications, such as electronic business, satellite remote sensing, intrusion discovery, and network traffic monitoring, generate large unbounded data stream sequences at a rapid pace.
Mustafa Tareq +3 more
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Trajectory Clustering Algorithm Based on Group and Density [PDF]
Jisuanji gongcheng, 2021 The existing density-based clustering methods are mainly used for point data clustering, and not suitable for large-scale trajectory data. To address the problem, this paper proposes a trajectory clustering algorithm based on group and density. According
YU Qingying, ZHAO Yajun, YE Zitong, HU Fan, XIA Yun
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Deep density-based image clustering [PDF]
Knowledge-Based Systems, 2020 Recently, deep clustering, which is able to perform feature learning that favors clustering tasks via deep neural networks, has achieved remarkable performance in image clustering applications. However, the existing deep clustering algorithms generally need the number of clusters in advance, which is usually unknown in real-world tasks.
Ren, Yazhou +3 more
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Density propagation based adaptive multi-density clustering algorithm. [PDF]
PLoS ONE, 2018 The performance of density based clustering algorithms may be greatly influenced by the chosen parameter values, and achieving optimal or near optimal results very much depends on empirical knowledge obtained from previous experiments.
Yizhang Wang, Wei Pang, You Zhou
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Density Backbone Clustering Algorithm Based on Adaptive Threshold [PDF]
Jisuanji kexue yu tansuo, 2023 The existing clustering algorithms are inaccurate to identify arbitrary clusters, sensitive to density changes within clusters, sensitive to outliers and difficult to determine the threshold.
ZHANG Jinhong, CHEN Mei, ZHANG Chi
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Effective Density-Based Clustering Algorithms for Incomplete Data
Big Data Mining and Analytics, 2021 Density-based clustering is an important category among clustering algorithms. In real applications, many datasets suffer from incompleteness. Traditional imputation technologies or other techniques for handling missing values are not suitable for ...
Zhonghao Xue, Hongzhi Wang
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Incremental Density-Based Clustering on Multicore Processors [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 The density-based clustering algorithm is a fundamental data clustering technique with many real-world applications. However, when the database is frequently changed, how to effectively update clustering results rather than reclustering from scratch remains a challenging task.
Son T. Mai +6 more
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Density-Based Clustering of Social Networks
Journal of the Royal Statistical Society Series A: Statistics in Society, 2022 Abstract The idea of the modal formulation of density-based clustering is to associate groups with the regions around the modes of the probability density function underlying the data. The correspondence between clusters and dense regions in the sample space is here exploited to discuss an extension of this approach to the analysis of ...
Menardi Giovanna, De stefano, Domenico
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Piecemeal Clustering: a Self-Driven Data Clustering Algorithm
IEEE Access, 2022 Various approaches have been discussed in the literature for the clustering of data, such as partitioning, hierarchical, and machine learning methods.
Md. Monjur Ul Hasan +4 more
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Density Peak Clustering Based on Relative Density under Progressive Allocation Strategy
Mathematical and Computational Applications, 2022 In traditional density peak clustering, when the density distribution of samples in a dataset is uneven, the density peak points are often concentrated in the region with dense sample distribution, which is easy to affect clustering accuracy.
Yongli Liu, Congcong Zhao, Hao Chao
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