Details

Autor(en) / Beteiligte

• Chen, Bo
• Ting, Kai Ming
• Washio, Takashi
• Zhu, Ye

Titel

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

Ist Teil von

• Machine learning, 2018-09, Vol.107 (8-10), p.1621-1645

Ort / Verlag

New York: Springer US

Erscheinungsjahr

2018

Link zum Volltext

Quelle

SpringerLink (Online service)

Beschreibungen/Notizen

• 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.