Abstract
Feature selection is one of the recent techniques in data mining. Clustering is an unsupervised learning method in knowledge discovery world. Dimensionality reduction poses electing the significant data from the wider variety of data elements. “Curse of dimensionality “is a challenging issue of present researches which produces wrong outcomes during clustering process. In this paper, cluster based outlier detection method is applied with various multivariate datasets. Before clustering the datasets, the feature selection method has been implemented for selecting significant datasets from the entire training attributes. Feature selection plays an essential role in cluster accuracy for obtaining the dissimilar and dissimilar datasets among the data instances. In this research work, the proposed system is compared with the various correlation based feature selection algorithms and their experimental results are depicted.
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