Extracting High Dimensional Data by Using Fast Clustering Based Feature Subset Selection System
This paper presents the feature assortment involve identifying a subset of the majority valuable features with the intention of produce companionable results the same as the inventive complete set of features. A feature assortment system could exist evaluate commencing both the effectiveness and efficiency points of analysis. Although the effectiveness concerns the point in time necessary in the direction of locate a subset of features, the efficiency is linked toward the excellence of the subset of features. Based on this criterion a fast clustering based feature selection system is planned along with experimentally evaluated within this paper. The fast system installation into two steps, during the initial step features are separated into cluster by means of using graph theoretic clustering method. In the subsequent step the essentially representative feature so as to be robustly correlated to objective module is elected on or after every cluster to appearance a subset of features. Features in dissimilar clusters are moderately supreme the clustering based approaches of fast have an elevated possibility of producing a subset of helpful with sovereign features. To make sure the effectiveness of fast we assume the competent bare minimum spanning tree clustering method. The effectiveness and efficiency of the fast system be evaluated throughout an experimental revise.
Keywords: FAST Clustering, Subset Selection Algorithm, Distributed Clustering, Spanning tree, Sensitivity Analysis
Cite this Article
Koteeswaran C, Ramesh Babu GV, Padmavathamma M. Extracting High Dimensional Data by Using Fast Clustering Based Feature Subset Selection System. Journal of Software Engineering Tools & Technology Trends. 2016; 3(3): 1–13p.
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