A semi-supervised approach to projected clustering with applications to microarray data.

Kevin Y. Yip*, Lin Cheung, David W. Cheung, Liping Jing, Kwok Po NG

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Recent studies have suggested that extremely low dimensional projected clusters exist in real datasets. Here, we propose a new algorithm for identifying them. It combines object clustering and dimension selection, and allows the input of domain knowledge in guiding the clustering process. Theoretical and experimental results show that even a small amount of input knowledge could already help detect clusters with only 1% of the relevant dimensions. We also show that this semi-supervised algorithm can perform knowledge-guided selective clustering when there are multiple meaningful object groupings. The algorithm is also shown effective in analysing a microarray dataset.

Original languageEnglish
Pages (from-to)229-259
Number of pages31
JournalInternational Journal of Data Mining and Bioinformatics
Volume3
Issue number3
DOIs
Publication statusPublished - 2009

Scopus Subject Areas

  • Information Systems
  • Biochemistry, Genetics and Molecular Biology(all)
  • Library and Information Sciences

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