Fast density peaks clustering algorithm based on improved mutual K-nearest-neighbor and sub-cluster merging

Density peaks clustering (DPC) has had an impact in many fields, as it can quickly select centers and effectively process complex data. However, it also has low operational efficiency and a “Domino” effect. To solve these defects, we propose a fast density peaks clustering algorithm based on improve...

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Bibliographic Details
Published in:Information sciences Vol. 647; p. 119470
Main Authors: Li, Chao, Ding, Shifei, Xu, Xiao, Hou, Haiwei, Ding, Ling
Format: Journal Article
Language:English
Published: Elsevier Inc 01.11.2023
Subjects:
Density peaks clustering
Improved mutual K-nearest-neighbor
Kd-tree
Sparse matrix
Sub-cluster merging
Sparse matrix
Sub-cluster merging
Kd-tree
Improved mutual K-nearest-neighbor
Density peaks clustering
ISSN:0020-0255
Online Access:Get full text
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Summary:Density peaks clustering (DPC) has had an impact in many fields, as it can quickly select centers and effectively process complex data. However, it also has low operational efficiency and a “Domino” effect. To solve these defects, we propose a fast density peaks clustering algorithm based on improved mutual K-nearest-neighbor and sub-cluster merging (KS-FDPC). The new algorithm adopts a partitioning-merging strategy. By dividing the data into multiple sub-clusters, the impact range of high-density points on subsequent allocation points can be reduced. And the fast nearest neighbors search improves the speed of DPC. In the experiment, KS-FDPC is used to compare with eight improved DPC algorithms on eight synthetic data and eight UCI data. The results indicate that the overall clustering performance of KS-FDPC is superior to other algorithms. Moreover, KS-FDPC runs faster than other algorithms. Therefore, KS-FDPC is an effective improvement of DPC.
ISSN:0020-0255
DOI:10.1016/j.ins.2023.119470