Constrained clustering for gene expression data mining

S. Tseng, Lien Chin Chen, Ching Pin Kao

研究成果: Conference contribution同行評審

2 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題Advances in Knowledge Discovery and Data Mining - 12th Pacific-Asia Conference, PAKDD 2008, Proceedings
頁面759-766
頁數8
DOIs
出版狀態Published - 2008
事件12th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2008 - Osaka, Japan
持續時間: 20 5月 200823 5月 2008

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
5012 LNAI
ISSN(列印)0302-9743
ISSN(電子)1611-3349

Conference

Conference12th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2008
國家/地區Japan
城市Osaka
期間20/05/0823/05/08

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