K-maximin clustering: a maximin correlation approach to partition-based clustering
- Authors
- Lee, Taehoon; Kim, Seung Jean; Chung, Eui-Young; Yoon, Sungroh
- Issue Date
- 10-9월-2009
- Publisher
- IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG
- Keywords
- data mining; clustering; maximin correlation; k-means
- Citation
- IEICE ELECTRONICS EXPRESS, v.6, no.17, pp.1205 - 1211
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEICE ELECTRONICS EXPRESS
- Volume
- 6
- Number
- 17
- Start Page
- 1205
- End Page
- 1211
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/119322
- DOI
- 10.1587/elex.6.1205
- ISSN
- 1349-2543
- Abstract
- We propose a new clustering algorithm based upon the maximin correlation analysis (MCA), a learning technique that can minimize the maximum misclassification risk. The proposed algorithm resembles conventional partition clustering algorithms such as k-means in that data objects are partitioned into k disjoint partitions. On the other hand, the proposed approach is unique in that an MCA-based approach is used to decide the location of the representative point for each partition. We test the proposed technique with typography data and show our approach outperforms the popular k-means and k-medoids clustering in terms of retrieving the inherent cluster membership.
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