• COMPARATIVE PERFORMANCE OF PARTITIONING ALGORITHMS

OMBIR DAHIYA*, SIKANDER AND RAVINDER KUMAR, JAGAT SINGH

Abstract


Data mining is a search for relationship and patterns that exist in large database. Clustering is an important data mining technique. Because of the complexity and the high dimensionality of gene expression data, classification of a disease samples remains a challenge. Hierarchical clustering and partitioning clustering is used to identify patterns of gene expression useful for classification of samples.  In order to explore the strength and weaknesses an attempt has been made to compare some of the existing variation of k-mean algorithms using high dimensional cancer datasets  as benchmark for evaluation and some criteria is also evolved for comparison of clustering algorithms.


Keywords


Clustering, data mining, k-mean, high dimensional databases.

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© 2010-2016 International Journal of Mathematical Archive (IJMA)
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