Machine Learning

, Volume 107, Issue 8–10, pp 1621–1645 | Cite as

Local contrast as an effective means to robust clustering against varying densities

  • Bo Chen
  • Kai Ming Ting
  • Takashi Washio
  • Ye Zhu
Part of the following topical collections:
  1. Special Issue of the ECML PKDD 2018 Journal Track


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.


Local contrast Density-based clustering Varying densities 



Bo Chen is supported by Monash Data61 Postgraduate Research Scholarship and Faculty of IT Tuition Fee Scholarship, Monash University.


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Copyright information

© The Author(s) 2018

Authors and Affiliations

  1. 1.Faculty of Information TechnologyMonash UniversityClaytonAustralia
  2. 2.School of Engineering and Information TechnologyFederation University AustraliaChurchillAustralia
  3. 3.The Institute of Scientific and Industrial ResearchOsaka UniversityIbarakishiJapan
  4. 4.School of Information TechnologyDeakin UniversityBurwoodAustralia

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