K-maximin clustering: A maximin correlation approach to partition-based clustering

Taehoon Lee, Seung Jean Kim, Eui Young Chung, Sungroh Yoon

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1205-1211
Number of pages7
Journalieice electronics express
Issue number17
Publication statusPublished - 2009 Sept 10

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Electrical and Electronic Engineering


Dive into the research topics of 'K-maximin clustering: A maximin correlation approach to partition-based clustering'. Together they form a unique fingerprint.

Cite this