Density propagation based adaptive multi-density clustering algorithm

PloS One
Yizhang WangYou Zhou

Abstract

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. To address this limitation, we propose a novel density based clustering algorithm called the Density Propagation based Adaptive Multi-density clustering (DPAM) algorithm. DPAM can adaptively cluster spatial data. In order to avoid manual intervention when choosing parameters of density clustering and still achieve high performance, DPAM performs clustering in three stages: (1) generate the micro-clusters graph, (2) density propagation with redefinition of between-class margin and intra-class cohesion, and (3) calculate regional density. Experimental results demonstrated that DPAM could achieve better performance than several state-of-the-art density clustering algorithms in most of the tested cases, the ability of no parameters needing to be adjusted enables the proposed algorithm to achieve promising performance.

References

Jan 16, 2007·Science·Brendan J Frey, Delbert Dueck
Jul 23, 2013·IEEE Transactions on Pattern Analysis and Machine Intelligence·Chang-Dong WangJun-Yong Zhu
Jun 28, 2014·Science·Alex Rodriguez, Alessandro Laio
Jul 11, 2018·IEEE Journal of Biomedical and Health Informatics·Xiangtao Li, Ka-Chun Wong
Jul 12, 2018·IEEE Transactions on Cybernetics·Xiangtao Li, Ka-Chun Wong

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Software Mentioned

DENCLUE
OPTICS
DP
DBSCAN
Spiral
CLIQUE
Clustering
DPAM
STING

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