Fast and effective text mining using linear-time document clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering analysis of microarray gene expression data by splitting algorithm
Journal of Parallel and Distributed Computing - High-performance computational biology
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Microarrays are one of the most recent ameliorations in experimental molecular biology. Handling and analysis of microarray data is a most challenging task. The cluster analysis is one of the important high level analysis techniques, often exploited for microarray data analysis. As proteins usually related with different groups of proteins in order to handle diverse biological roles, the genes that create such proteins are thus expected to interact with more than one group of genes. This construes that in micro array gene expression data, a gene may make its presence in more than one cluster. The prior research has expressed the presence of genes in one or more clusters consistent with the nature of the gene and its attributes by the two dimensional clustering technique. The competence of the clustering analysis depends on the designing of an efficient (dis) similarity measure for grouping them. This research, has improved the prior cluster analysis research via an efficient hybrid distance based similarity measure. The proposed technique is implemented and its performance is evaluated with microarray gene expression data.