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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chandrasekhar, T. Thangavel, K. Elayaraja, E. |
| Copyright Year | 2011 |
| Description | Author affiliation: Department of Computer Science, Periyar university Salem, TamilNadu, India - 636 011 (Chandrasekhar, T.; Thangavel, K.; Elayaraja, E.) |
| Abstract | Microarrays are made it possible to simultaneously monitor the expression profiles of thousands of genes under various experimental conditions. Identification of co-expressed genes and coherent patterns is the central goal in microarray or gene expression data analysis and is an important task in bioinformatics research. In this work the unsupervised Gene selection methods and CCIA with K-Means algorithms have been applied for clustering of Gene Expression Data. This proposed clustering algorithm overcomes the drawbacks in terms of specifying the optimal number of clusters and initialization of good cluster centroids. Gene Expression Data show that could identify compact clusters with performs well in terms of the Silhouette Coefficients cluster measure. |
| Starting Page | 146 |
| Ending Page | 150 |
| File Size | 134492 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781467306706 |
| e-ISBN | 9781467306713 |
| DOI | 10.1109/ICoAC.2011.6165164 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-12-14 |
| Publisher Place | India |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Algorithm design and analysis Computer science K-Means Clustering algorithms CCIA Classification algorithms Gene expression data Gene expression Data mining Clustering Unsupervised Feature Selection Bioinformatics |
| Content Type | Text |
| Resource Type | Article |
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