CSTuEPM: An Efficient Clustering Algorithm for Micro Array Gene Expression Data

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Muhammad Rukunuddin Ghalib


Clustering analysis has been an important research topic in the machine learning field due to its wide applications in the area of data
mining and bioinformatics. In recent years, it has even become a valuable and useful tool for in-silico analysis of microarray data. Although a
number of clustering methods have been proposed, they are confronted with difficulties in meeting the requirements of automation, high cluster
quality, and efficiency. An efficient clustering algorithm, namely, CSTuEPM (Correlation Search Technique using Euclidean proximity
measure), which fits for analysis of gene expression data is proposed. The unique feature of this approach is that it incorporates the validation
techniques into the clustering process so that high quality clustering results can be produced. The proposed work aims in incorporating Euclidean
Proximity measure for measuring the similarity (or distance) between two data objects with ease.


Keywords: Data Mining, Cluster Analysis, Gene expression analysis, Micro Array Analysis


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