- Title
- Local contrast as an effective means to robust clustering against varying densities
- Creator
- Chen, Bo; Ting, Kaiming; Washio, Takashi; Zhu, Ye
- Date
- 2018
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/165640
- Identifier
- vital:13282
- Identifier
-
https://doi.org/10.1007/s10994-017-5693-x
- Identifier
- ISBN:0885-6125
- Abstract
- 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. © 2018, The Author(s).
- Publisher
- Springer New York LLC
- Relation
- Machine Learning Vol. 107, no. 8-10 (2018), p. 1621-1645
- Rights
- Copyright © 2018, The Author(s).
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0801 Artificial Intelligence and Image Processing; 0806 Information Systems; 1702 Cognitive Science; Density-based clustering; Local contrast; Varying densities
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