A novel parameter-less clustering method for mining gene expression data

S. Tseng, Ching Pin Kao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Clustering analysis has been applied in a wide variety of fields. In recent years, it has even become a valuable and useful technique for in-silico analysis of microarray or gene expression data. Although a number of clustering methods have been proposed, they are confronted with difficulties in the requirements of automation, high quality, and high efficiency at the same time. In this paper, we explore the issue of integration between clustering methods and validation techniques. We propose a novel, parameter-less, and efficient clustering algorithm, namely CST, which is suitable for analysis of gene expression data. Through experimental evaluation, CST is shown to outperform other clustering methods substantially in terms of clustering quality, efficiency, and automation under various types of datasets.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings
EditorsHonghua Dai, Chengqi Zhang, Ramakrishnan Srikant
PublisherSpringer Verlag
Pages692-698
Number of pages7
ISBN (Print)354022064X, 9783540220640
DOIs
StatePublished - 1 Jan 2004
Event8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004 - Sydney, Australia
Duration: 26 May 200428 May 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3056
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004
CountryAustralia
CitySydney
Period26/05/0428/05/04

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