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Research Article Open Access
A database can contain several dimensions or attributes. Many Clustering methods are designed for clustering low–dimensional data. In high dimensional space finding clusters of data objects is challenging due to the curse of dimensionality. When the dimensionality increases, data in the irrelevant dimensions may produce much noise and mask the real clusters to be discovered. To deal with these problems, an efficient feature subset selection technique for high dimensional data has been proposed. Feature subset selection reduces the data size by removing irrelevant or redundant attributes. This algorithm works in two different steps that is minimum spanning tree based clustering methods and representative feature cluster selection. The proposed Pearson correlation measure focused on minimized redundant data. As a result, only a small number of discriminative features are selected.