A clustering algorithm based on density decreased chain for data with arbitrary shapes and densities

Li, Ruijia; Cai, Zixing

doi:10.1007/s10489-022-03583-4

Cited by 6 publications

(1 citation statement)

References 44 publications

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“…Clustering aims to maximize data similarity within clusters while minimizing similarity between different clusters [4,5]. Until now, previous researchers have proposed lots of classic clustering algorithms [6][7][8][9][10][11][12][13]. For instance, clustering algorithms like Kmeans [14], DBSCAN [15], and FCM [16] are widely recognized.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical clustering algorithm based on natural local density peaks

Cai,

Feng,

Yang

et al. 2024

Preprint

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Natural neighbor is known for its ability to adaptively identify the nearest neighbors in a dataset, but it sometimes can not accurately cluster in datasets with density variations. To overcome this drawback, this paper proposes a hierarchical clustering algorithm based on natural local density peaks (HC-NLDP). HC-NLDP mainly consists of partition and merge. Firstly, HC-NLDP searches natural local density peaks to form sub-clusters. This effectively solves the problem that points in low-density regions are difficult to cluster accurately. Secondly, HC-NLDP computes the shared similarity between every pair of sub-clusters. The shared similarity describes the relevance between sub-clusters. When confronted with datasets exhibiting density variations, the shared similarity can ensure that HC-NLDP achieves superior clustering performance. Numerical experiments based on synthetic and real datasets demonstrate the effectiveness and superiority of HC-NLDP.

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