Title :
Cluster structure inference for microarray data based on an information theoretic criterion
Author :
Nicorici, Daniel ; Yli-Harja, Olli ; Astola, Jaakko
Author_Institution :
Inst. of Signal Process., Tampere Univ. of Technol., Tampere
Abstract :
This paper presents a new method for estimating the number of clusters and cluster structure in microarray data sets, based on minimum description length (MDL) principle and normalized maximum likelihood (NML) model for linear regression. The cluster-structure models for microarray data are compared based on the MDL principle. The performance of the new method is studied using simulated and real microarray data sets.
Keywords :
biology computing; maximum likelihood estimation; pattern clustering; regression analysis; cluster structure inference; information theoretic criterion; linear regression; microarray data; minimum description length principle; normalized maximum likelihood model; Clustering algorithms; Data analysis; Electronic mail; Encoding; Gene expression; Linear regression; Maximum likelihood estimation; Signal processing; Signal processing algorithms; Vectors;
Conference_Titel :
Genomic Signal Processing and Statistics, 2006. GENSIPS '06. IEEE International Workshop on
Conference_Location :
College Station, TX
Print_ISBN :
1-4244-0384-7
Electronic_ISBN :
1-4244-0385-5
DOI :
10.1109/GENSIPS.2006.353169
