Density propagation based adaptive multi-density clustering algorithm

Yizhang Wang, Wei Pang, You Zhou (Corresponding Author)

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9 Citations (Scopus)
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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.
Original languageEnglish
Article numbere0198948
Pages (from-to)1-13
Number of pages13
JournalPloS ONE
Issue number7
Publication statusPublished - 18 Jul 2018

Bibliographical note

This research was supported by the Science & Technology Development Foundation of Jilin Province (Grants Nos. 20160101259JC, 20180201045GX), the National Natural Science Foundation of China (Grants No. 61772227) and the Natural Science
Foundation of Xinjiang Province (Grants No. 2015211C127). This resarch is also
supported by the Engineering and Physical Sciences Research Council (EPSRC) funded project on New Industrial Systems: Manufacturing Immortality (EP/R020957/1).


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