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Journal of Applied Sciences
  Year: 2012 | Volume: 12 | Issue: 18 | Page No.: 1932-1938
DOI: 10.3923/jas.2012.1932.1938
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An Intelligent Mining Framework based on Rough Sets for Clustering Gene Expression Data

J. Jeba Emilyn and K. Ramar

The main aim of this study is not only to develop a biclustering algorithm that would successfully identify gene patterns but also to propose an intelligent clustering framework that would improve the cluster quality. Our framework for mining co-regulated genes from gene expression dataset is composed of three important steps: a preprocessing step to refine the data, an intelligent procedure to predict the possible number of biclusters and a procedure based on rough sets to cluster the gene datasets. Our algorithm is said to be intelligent, in the sense that it can predict the possible number of biclusters. Since, the algorithm is based on rough sets, there are high possibilities of placing a gene in more than one bicluster and thus allows overlapping of biclusters. A theoretical understanding of the proposed algorithm is analyzed and results are illustrated with different gene expression data sets. The analysis and the experiment shows that the method is more intelligent and efficient.
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  •    Integrated Approach of Reduct and Clustering for Mining Patterns from Clusters
  •    Variations of k-mean Algorithm: A Study for High-Dimensional Large Data Sets
  •    A Semaphore Based Multiprocessing k-Mean Algorithm for Massive Biological Data
  •    Clustering Methods for Statistical Analysis of Genome Databases
How to cite this article:

J. Jeba Emilyn and K. Ramar, 2012. An Intelligent Mining Framework based on Rough Sets for Clustering Gene Expression Data. Journal of Applied Sciences, 12: 1932-1938.

DOI: 10.3923/jas.2012.1932.1938






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