@Article{10798587.2017.1307626,
AUTHOR = {U. Kanimozhi, D. Manjula},
TITLE = {An Intelligent Incremental Filtering Feature Selection and Clustering Algorithm for  Effective Classification},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {24},
YEAR = {2018},
NUMBER = {4},
PAGES = {701--709},
URL = {http://www.techscience.com/iasc/v24n4/39796},
ISSN = {2326-005X},
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.},
DOI = {10.1080/10798587.2017.1307626}
}