BibTeX:

@article{IJIRSTV1I9044,
     title={Proficient System for Feature Subset Selection in High Dimensional Data},
     author={V.Thirumani Thangam and P.Senthil Kumari},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={1},
     number={9},
     pages={156--163},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV1I9044.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

Feature selection is widely used in preparing high-dimensional data for effective data mining. Getting fast popularity in the social media dataset presents new challenges for feature selection. Web search media data consists of traditional high- dimensional, attribute-value data such as posts, tweets, comments, and images and linked data. The FAST algorithm applies to on data set and produces smaller subsets of features and also improves the accuracy. The FAST not only produces smaller subsets of features but also improves the performances of the process. Experimental results show that our FAST algorithm implementation can run faster and obtain better-extracted features than other methods.

Feature selection, FAST, subset, clustering, filter method