Open Access
ARTICLE
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,
Intelligent Automation & Soft Computing 2018, 24(4), 701-709. https://doi.org/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 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.
Keywords
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. https://doi.org/10.1080/10798587.2017.1307626