Constraint based Clustering in Feature Subset Selection Algorithm
S Aswini, A Kumaresan, K Vijayakumar and D Murali. Article: Constraint based Clustering in Feature Subset Selection Algorithm. IJCA Proceedings on National Conference cum Workshop on Bioinformatics and Computational Biology NCWBCB(1):1-6, May 2014. Full text available. BibTeX
@article{key:article,
author = {S. Aswini and A. Kumaresan and K. Vijayakumar and D. Murali},
title = {Article: Constraint based Clustering in Feature Subset Selection Algorithm},
journal = {IJCA Proceedings on National Conference cum Workshop on Bioinformatics and Computational Biology},
year = {2014},
volume = {NCWBCB},
number = {1},
pages = {1-6},
month = {May},
note = {Full text available}
}
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).
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