Welcome to the InfoSci Platform
Soft Subspace Clustering for Cancer Microarray Data Analysis: A Survey

Soft Subspace Clustering for Cancer Microarray Data Analysis: A Survey

Natthakan Iam-On, Tossapon Boongoen
Copyright: © 2014 |Pages: 15
ISBN13: 9781466649361|ISBN10: 1466649364|EISBN13: 9781466649378
DOI: 10.4018/978-1-4666-4936-1.ch006
Cite Chapter Cite Chapter

MLA

Iam-On, Natthakan, and Tossapon Boongoen. "Soft Subspace Clustering for Cancer Microarray Data Analysis: A Survey." Global Trends in Intelligent Computing Research and Development, edited by B.K. Tripathy and D. P. Acharjya, IGI Global, 2014, pp. 131-145. https://doi.org/10.4018/978-1-4666-4936-1.ch006

APA

Iam-On, N. & Boongoen, T. (2014). Soft Subspace Clustering for Cancer Microarray Data Analysis: A Survey. In B. Tripathy & D. Acharjya (Eds.), Global Trends in Intelligent Computing Research and Development (pp. 131-145). IGI Global. https://doi.org/10.4018/978-1-4666-4936-1.ch006

Chicago

Iam-On, Natthakan, and Tossapon Boongoen. "Soft Subspace Clustering for Cancer Microarray Data Analysis: A Survey." In Global Trends in Intelligent Computing Research and Development, edited by B.K. Tripathy and D. P. Acharjya, 131-145. Hershey, PA: IGI Global, 2014. https://doi.org/10.4018/978-1-4666-4936-1.ch006

Export Reference

Mendeley
Favorite

Abstract

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.