Title :
Clustering, Assessment and Validation: an application to gene expression data
Author :
Ciaramella, A. ; Cocozza, S. ; Iorio, F. ; Miele, G. ; Napolitano, F. ; Pinelli, M. ; Raiconi, G. ; Tagliaferri, R.
Author_Institution :
DMI, Salerno Univ., Salerno
Abstract :
In this work a multi-step approach for clustering assessment, visualization and data validation is introduced. Three main approaches for data clustering are used and compared: K-means, self organizing maps and probabilistic principal surfaces. A model explorer approach with different similarity measures is used to obtain the best parameters of the methods. The approach is used to identify genes periodically expressed in tumors related to the human cell cycle. Finally, clusters are validated by using GO term information.
Keywords :
biology computing; data visualisation; genetics; pattern clustering; tumours; GO Term information; K-means clustering; data assessment; data clustering; data validation; data visualization; gene expression data; human cell cycle; model explorer approach; probabilistic principal surfaces; self organizing maps; tumors; Bioinformatics; Cells (biology); Data mining; Data visualization; Gene expression; Genetics; Genomics; Humans; Independent component analysis; Principal component analysis;
Conference_Titel :
Neural Networks, 2007. IJCNN 2007. International Joint Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
978-1-4244-1379-9
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2007.4371199