Soft subspace clustering for cancer microarray data analysis: A survey

Natthakan Iam-On*, Tossapon Boongoen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter


A need has long been identified for a more effective methodology to understand, prevent, and cure cancer. Microarray technology provides a basis of achieving this goal, with cluster analysis of gene expression data leading to the discrimination of patients, identification of possible tumor subtypes, and individualized treatment. Recently, soft subspace clustering was introduced as an accurate alternative to conventional techniques. This practice has proven effective for high dimensional data, especially for microarray gene expressions. In this review, the basis of weighted dimensional space and different approaches to soft subspace clustering are described. Since most of the models are parameterized, the application of consensus clustering has been identified as a new research direction that is capable of turning the difficulty with parameter selection to an advantage of increasing diversity within an ensemble.

Original languageEnglish
Title of host publicationGlobal Trends in Intelligent Computing Research and Development
EditorsB. K. Tripathy, D. P. Acharjya
PublisherIGI Global Publishing
Number of pages15
ISBN (Electronic)9781466649378
ISBN (Print)1466649364, 9781466649361
Publication statusPublished - 31 Dec 2013
Externally publishedYes

Publication series

NameAdvances in Computational Intelligence and Robotics


Dive into the research topics of 'Soft subspace clustering for cancer microarray data analysis: A survey'. Together they form a unique fingerprint.

Cite this