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Reseach Article

Constraint based Clustering in Feature Subset Selection Algorithm

Published on May 2014 by S. Aswini, A. Kumaresan, K. Vijayakumar, D. Murali
National Conference cum Workshop on Bioinformatics and Computational Biology
Foundation of Computer Science USA
NCWBCB - Number 1
May 2014
Authors: S. Aswini, A. Kumaresan, K. Vijayakumar, D. Murali
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S. Aswini, A. Kumaresan, K. Vijayakumar, D. Murali . Constraint based Clustering in Feature Subset Selection Algorithm. National Conference cum Workshop on Bioinformatics and Computational Biology. NCWBCB, 1 (May 2014), 1-6.

@article{
author = { S. Aswini, A. Kumaresan, K. Vijayakumar, D. Murali },
title = { Constraint based Clustering in Feature Subset Selection Algorithm },
journal = { National Conference cum Workshop on Bioinformatics and Computational Biology },
issue_date = { May 2014 },
volume = { NCWBCB },
number = { 1 },
month = { May },
year = { 2014 },
issn = 0975-8887,
pages = { 1-6 },
numpages = 6,
url = { /proceedings/ncwbcb/number1/16503-1401/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference cum Workshop on Bioinformatics and Computational Biology
%A S. Aswini
%A A. Kumaresan
%A K. Vijayakumar
%A D. Murali
%T Constraint based Clustering in Feature Subset Selection Algorithm
%J National Conference cum Workshop on Bioinformatics and Computational Biology
%@ 0975-8887
%V NCWBCB
%N 1
%P 1-6
%D 2014
%I International Journal of Computer Applications
Abstract

Constraint based clustering that satisfies set of user-defined constraint. Constraint-based clustering is an example of a mining task where flexibility is desirable. It is a generalization of standard clustering in which the user can impose constraints on the clustering to be found, such as similar and dissimilar constraints. Feature selection involves recognizing a subset of the most constructive features that produces companionable outcomes as the innovative intact set of traits. Feature selection, as a data for preliminary considered. Features are processing step is effectual in reducing the spatial property, confiscate irrelevant data, mounting learning accuracy. We proposed an algorithm for the Region of Influence (ROI). The algorithm is numb to the order in which the pairs are divided into cluster by using relative neighborhood graphs (RNGs).

References
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Index Terms

Computer Science
Information Sciences

Keywords

Relative Neighborhood Graphs Region Of Influence Feature Subset Selection.