A Novel Granularity Optimal Feature Selection based on Multi-Variant Clustering for High Dimensional Data

Abstract

Clustering is the most complex in multi/high dimensional data because of sub feature selection fromoverall features present in categorical data sources. Sub set feature be the aggressive approach to decreasefeature dimensionality in mining of data, identification of patterns. Main aim behind selection of feature withrespect to selection of optimal feature and decrease the redundancy. In-order to compute withredundant/irrelevant features in high dimensional sample data exploration based on feature selection calculationwith data granular described in this document. Propose aNovel Granular Feature Multi-variant Clustering basedGenetic Algorithm (NGFMCGA) model to evaluate the performance results in this implementation. This modelmain consists two phases, in first phase, based on theoretic graph grouping procedure divide features intodifferent clusters, in second phase, select strongly representative related feature from each cluster with respectto matching of subset of features. Features present in this concept are independent because of features selectfrom different clusters, proposed approach clustering have high probability in processing and increasing thequality of independent and useful features.Optimal subset feature selection improves accuracy of clustering andfeature classification, performance of proposed approach describes better accuracy with respect to optimalsubset selection is applied on publicly related data sets and it is compared with traditional supervisedevolutionary approaches.