Local contrast as an effective means to robust clustering against varying densities
Most density-based clustering methods have difficulties detecting clusters of hugely different densities in a dataset. A recent density-based clustering CFSFDP appears to have mitigated the issue. However, through formalising the condition under which it fails, we reveal that CFSFDP still has the sa...
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Veröffentlicht in: | Machine learning 2018-09, Vol.107 (8-10), p.1621-1645 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Most density-based clustering methods have difficulties detecting clusters of hugely different densities in a dataset. A recent density-based clustering CFSFDP appears to have mitigated the issue. However, through formalising the condition under which it fails, we reveal that CFSFDP still has the same issue. To address this issue, we propose a new measure called Local Contrast, as an alternative to density, to find cluster centers and detect clusters. We then apply Local Contrast to CFSFDP, and create a new clustering method called LC-CFSFDP which is robust in the presence of varying densities. Our empirical evaluation shows that LC-CFSFDP outperforms CFSFDP and three other state-of-the-art variants of CFSFDP. |
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ISSN: | 0885-6125 1573-0565 |
DOI: | 10.1007/s10994-017-5693-x |