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An Intelligent Incremental Filtering Feature Selection and Clustering Algorithm for Effective Classification

U. Kanimozhi, D. Manjula

Department of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai, Tamil Nadu, India

* Corresponding Author: U. Kanimozhi, email

Intelligent Automation & Soft Computing 2018, 24(4), 701-709.


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 grouping of large volumes of data, respectively. An extensive experimental comparison of the proposed method and other methods are done using four different classifiers. The performance of the proposed algorithms yields promising results on the feature selection, clustering and classification accuracy in the field of biomedical data mining.


Cite This Article

U. Kanimozhi and D. Manjula, "An intelligent incremental filtering feature selection and clustering algorithm for effective classification," Intelligent Automation & Soft Computing, vol. 24, no.4, pp. 701–709, 2018.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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