Welcome to the InfoSci Platform
Fuzzy Clustering of Large Relational Bioinformatics Datasets

Fuzzy Clustering of Large Relational Bioinformatics Datasets

Mihail Popescu
Copyright: © 2010 |Pages: 21
ISBN13: 9781605668581|ISBN10: 1605668583|EISBN13: 9781605668598
DOI: 10.4018/978-1-60566-858-1.ch016
Cite Chapter Cite Chapter

MLA

Popescu, Mihail. "Fuzzy Clustering of Large Relational Bioinformatics Datasets." Scalable Fuzzy Algorithms for Data Management and Analysis: Methods and Design, edited by Anne Laurent and Marie-Jeanne Lesot, IGI Global, 2010, pp. 379-399. https://doi.org/10.4018/978-1-60566-858-1.ch016

APA

Popescu, M. (2010). Fuzzy Clustering of Large Relational Bioinformatics Datasets. In A. Laurent & M. Lesot (Eds.), Scalable Fuzzy Algorithms for Data Management and Analysis: Methods and Design (pp. 379-399). IGI Global. https://doi.org/10.4018/978-1-60566-858-1.ch016

Chicago

Popescu, Mihail. "Fuzzy Clustering of Large Relational Bioinformatics Datasets." In Scalable Fuzzy Algorithms for Data Management and Analysis: Methods and Design, edited by Anne Laurent and Marie-Jeanne Lesot, 379-399. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-858-1.ch016

Export Reference

Mendeley
Favorite

Abstract

In this chapter the author presents a fuzzy clustering methodology that can be employed for large relational datasets. Relational data is an N×N matrix that consists of pair-wise dissimilarities among N objects. Large relational datasets are encountered in many domains such as psychology or medical informatics, but they are abundant in bioinformatics where gene products are compared to each other based on various characteristics such as DNA or amino acid sequence. The fuzzy clustering methodology is exemplified on a set of about 30,000 human gene products.

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.