A Pragmatic Application of Clustering-Based Feature Subset Selection Algorithm for High Dimensional Data  
  Authors : N.Narendra Reddy; K. Narayana


Using the rapid growth of computational biology and e-commerce applications, high-dimensional data becomes usual. Thus, mining high dimensional data is an urgent problem of great practical importance. Within the high dimensional data the dimensional reduction is a vital factor, to the purpose the clustering based feature subset selection algorithm is proposed in this particular paper. The characteristics are actually clustered Based on the class labels. The Relevance on the clustered features has become evaluated. The correlation on the relevant clustered feature will be evaluated. This technique improved by cluster based FAST Algorithm and Fuzzy Logic. FAST Algorithm can often Identify and taking out the irrelevant data set. This algorithm process implements using two different steps which are graph theoretic clustering methods and representative feature cluster is selected. Feature subset selection researchers have centered on in search of relevant features. The proposed fuzzy logic has focused on minimized redundant data set and improves the feature subset accuracy.


Published In : IJCAT Journal Volume 1, Issue 7

Date of Publication : 31 August 2014

Pages : 349 - 353

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Publication Link : A Pragmatic Application of Clustering-Based Feature Subset Selection Algorithm for High Dimensional Data




N.Narendra Reddy : Post-Graduate Student, Department of Computer Science and Engineering, SIT, PUTTUR, India

K. Narayana : Head & Associate Professor, Department of computer Science and Engineering, SIT, PUTTUR, India









Fuzzy logic



In this particular paper, we have proposed a clusteringbased feature subset selection algorithm for high dimensional data. The algorithm involves 1) removing irrelevant features, 2) constructing the absolute minimum spanning tree from relative ones, and 3) partitioning the MST and selecting representative features. The projected feature subset selection algorithm FAST was tested as well as the investigational results demonstrate that, evaluated along with other various kinds of feature subset selection algorithms, the projected algorithm not only decrease the quantity of features, and also advances the performances with the renowned various kinds of classifiers.










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