Title :
Clustering of Gene Expression Data: Performance and Similarity Analysis
Author :
Yin, Longde ; Huang, Chun-Hsi
Author_Institution :
Dept. of Comput. Sci. & Eng., Connecticut Univ., Storrs, CT
Abstract :
Recent advances of the DNA microarray technology allow monitoring gene expression profiles of thousands of genes simultaneously. However, the analysis and handling of such fast growing data is becoming the major bottleneck in the utilization of the technology. Clustering analysis is one of the most effective methods for analyzing such gene expression data. In this paper we first experimentally study three major clustering algorithms: hierarchical clustering, self-organizing map (SOM), and self organizing tree algorithm (SOTA), using yeast saccharomyces cerevisiae gene expression data, and compare their performance. Then, we present a data mining tool, cluster diff, which allows the similarity analysis of clusters generated by different algorithms. A case study is conducted based on clusters generated by SOTA and SOM
Keywords :
DNA; biology computing; data mining; genetics; molecular biophysics; pattern clustering; self-organising feature maps; DNA microarray technology; SOM; SOTA; clustering analysis; data mining tool; hierarchical clustering; self organizing tree algorithm; self-organizing map; yeast saccharomyces cerevisiae gene expression data; Algorithm design and analysis; Chemical technology; Clustering algorithms; Clustering methods; DNA; Data mining; Fungi; Gene expression; Organizing; Performance analysis; Cluster Similarity Analysis; Clustering algorithms; Gene expression; Microarray; Performance study;
Conference_Titel :
Computer and Computational Sciences, 2006. IMSCCS '06. First International Multi-Symposiums on
Conference_Location :
Hanzhou, Zhejiang
Print_ISBN :
0-7695-2581-4
DOI :
10.1109/IMSCCS.2006.43
