A Fast Density Peak Clustering Method with Autoselect Cluster Centers
Author(s) -
Zhihe Wang,
Yongbiao Li,
Hui Du,
Xiaofen Wei
Publication year - 2022
Publication title -
mobile information systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.346
H-Index - 34
eISSN - 1875-905X
pISSN - 1574-017X
DOI - 10.1155/2022/4176101
Subject(s) - cluster analysis , cluster (spacecraft) , complete linkage clustering , selection (genetic algorithm) , computer science , data mining , boundary (topology) , position (finance) , k medians clustering , core (optical fiber) , single linkage clustering , pattern recognition (psychology) , mathematics , artificial intelligence , fuzzy clustering , cure data clustering algorithm , mathematical analysis , telecommunications , finance , economics , programming language
Aiming at density peaks clustering needs to manually select cluster centers, this paper proposes a fast new clustering method with auto-select cluster centers. Firstly, our method groups the data and marks each group as core or boundary groups according to its density. Secondly, it determines clusters by iteratively merging two core groups whose distance is less than the threshold and selects the cluster centers at the densest position in each cluster. Finally, it assigns boundary groups to the cluster corresponding to the nearest cluster center. Our method eliminates the need for the manual selection of cluster centers and improves clustering efficiency with the experimental results.
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