Block Mixture Model for the Biclustering of Microarray Data

Author

Saber, H.B. ; Elloumi, Mourad ; Nadif, Mohamed

Author_Institution

Unit of Res. of Technol. of Inf. & Commun., Univ. of Tunis, Tunis, Tunisia

fYear

2011

fDate

Aug. 29 2011-Sept. 2 2011

Firstpage

423

Lastpage

427

Abstract

An attractive way to make biclustering of genes and conditions is to adopt a Block Mixture Model (BMM). Approaches based on a BMM operate thanks to a Block Expectation Maximization (BEM) algorithm and/or a Block Classification Expectation Maximization (BCEM) one. The drawback of these approaches is their difficulty to choose a good strategy of initialization of the BEM and BCEM algorithms. This paper introduces existing biclustering approaches adopting a BMM and suggests a new fuzzy biclustering one. Our approach enables to choose a good strategy of initialization of the BEM and BCEM algorithms.

Keywords

expectation-maximisation algorithm; fuzzy set theory; pattern clustering; block classification expectation maximization; block expectation maximization algorithm; block mixture model; fuzzy biclustering; microarray data biclustering; Biological system modeling; Classification algorithms; Clustering algorithms; Educational institutions; Equations; Gene expression; Mathematical model; biclustering; block classification expectation maximization algorithm; block expectation maximization algorithm; block mixture model; fuzzy strategy; microarray data;

fLanguage

English

Publisher

ieee

Conference_Titel

Database and Expert Systems Applications (DEXA), 2011 22nd International Workshop on

Conference_Location

Toulouse

ISSN

1529-4188

Print_ISBN

978-1-4577-0982-1

Type

conf

DOI

10.1109/DEXA.2011.17

Filename

6059854