E-CAST: A Data Mining Algorithm for Gene Expression Data

Data clustering methods have been proven to be a successful data mining technique in the analysis of gene expression data. The Cluster affinity search technique (CAST) developed by Ben-Dor, et. al., 1999, which has been shown to cluster gene expression data well, has two drawbacks. First, the algorithm uses a fixed initial threshold value to start the clustering. As stated in the original paper, this parameter directly affects the size and number of clusters produced. Second, the algorithm requires a final cleaning step, which takes O(n2 ), to relocate n data points among the existing clusters. In this paper, we have developed and enhanced CAST algorithm, called E-CAST, that uses a dynamic threshold. The threshold value is computed at the beginning of each new cluster. We have implemented both CAST and E-CAST algorithms and tested their performance using three different data sets. The datasets are real gene expression data from melanoma, pheochromocytoma and brain cell tissue samples ...

• Type: Conference
• Year: 2002
• Where: KDD
• Authors: Abdelghani Bellaachia, David Portnoy, Yidong Chen, Abdel. G. Elkahloun
• Keywords: Data Clustering Methods, Data Mining, Gene Expression Data, KDD 2002, Original Cast Algorithm