U. Kanimozhi, D. Manjula
Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 701-709, 2018, DOI:10.1080/10798587.2017.1307626
Abstract We are witnessing the era of big data computing where computing the resources is becoming the main
bottleneck to deal with those large datasets. In the case of high-dimensional data where each view of
data is of high dimensionality, feature selection is necessary for further improving the clustering and
classification results. In this paper, we propose a new feature selection method, Incremental Filtering
Feature Selection (IF2S) algorithm, and a new clustering algorithm, Temporal Interval based Fuzzy
Minimal Clustering (TIFMC) algorithm that employs the Fuzzy Rough Set for selecting optimal subset
of features and for effective More >
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