Constrained clustering for gene expression data mining

S. Tseng, Lien Chin Chen, Ching Pin Kao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Constrained clustering algorithms have the advantage that domain-dependent constraints can be incorporated in clustering so as to achieve better clustering results. However, the existing constrained clustering algorithms are mostly k-means like methods, which may only deal with distance-based similarity measures. In this paper, we propose a constrained hierarchical clustering method, called Correlational-Constrained Complete Link (C-CCL), for gene expression analysis with the consideration of gene-pair constraints, while using correlation coefficients as the similarity measure. C-CCL was evaluated for the performance with the correlational version of COP-k-Means (C-CKM) method on a real yeast dataset. We evaluate both clustering methods with two validation measures and the results show that C-CCL outperforms C-CKM substantially in clustering quality.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 12th Pacific-Asia Conference, PAKDD 2008, Proceedings
Pages759-766
Number of pages8
DOIs
StatePublished - 9 Jun 2008
Event12th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2008 - Osaka, Japan
Duration: 20 May 200823 May 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5012 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2008
CountryJapan
CityOsaka
Period20/05/0823/05/08

Keywords

  • Constrained clustering
  • Gene expression mining
  • Hierarchical clustering
  • Micorarray analysis

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    Tseng, S., Chen, L. C., & Kao, C. P. (2008). Constrained clustering for gene expression data mining. In Advances in Knowledge Discovery and Data Mining - 12th Pacific-Asia Conference, PAKDD 2008, Proceedings (pp. 759-766). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5012 LNAI). https://doi.org/10.1007/978-3-540-68125-0_73