Paper Title
Superior Quality Of Feature Subset Selection Using Fast Clustering

Abstract—In the high dimensional data set having features selection involves identifying a subset of the most useful features that produce compatible results as the original entire set of features. A fast algorithm may be evaluated from both the efficiency and effectiveness of the subset of features. Fast clustering based feature selection is proposed for fast clustering in high dimensional data. In this algorithm features are divided into clusters and then the related independent classes of the subset is selected from each cluster to form a subset of features. Finally the fast clustering can be performed based on the minimum spanning tree method for ensuring the efficiency of fast clustering in the datasets.