Improvised Method Of FAST Clustering Based Feature Selection Technique Algorithm For High Dimensional Data

ABSTRACT A high dimensional data is data consisting thousands of attributes or features. Nowadays for scientific and research applications high dimensional data is used.But.as there are thousands of features present in the data, We need to select the features those are non-redundant and most relevant in order to reduce the dimensionality and runtime, and also improve accuracy of the results. In this paper we have proposed FAST algorithm of feature subset selection and improved method of FAST algorithm. The efficiency and accuracy of the results is evaluated by empirical study. In this paper, we have presented a novel clustering-based feature subset selection algorithm for high dimensional data. The algorithm involves (i) removing irrelevant features, (ii) constructing a minimum spanning tree from relative ones, and (iii) partitioning the MST and selecting representative features. In the proposed algorithm, a cluster consists of features. Each cluster is treated as a single feature and thus dimensionality is highly reduced. The Proposed System will be Implementation of FAST algorithm Using Dice Coefficient Measure to remove irrelevant and redundant features.