Enhanced neighborhood normalized pointwise mutual information algorithm for constraint aware data clustering

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Pushpa, C.N. and Gerard Deepak, . and Mohammed Zakir, . and Thriveni, J. and Venugopal, K.R. (2016) Enhanced neighborhood normalized pointwise mutual information algorithm for constraint aware data clustering. ICTACT Journal on Soft Computing, 6 (4). pp. 1287-1292. ISSN 2229-6956

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Official URL: http://10.21917/ijsc.2016.0176

Abstract

Clustering of similar data items is an important technique in mining useful patterns. To enhance the performance of Clustering, training or learning is an important task. A constraint learning semi-supervised methodology is proposed which incorporates SVM and Normalized Point wise Mutual Information Computation Strategy to increase the relevance as well as the performance efficiency of clustering. The SVM Classifier is of Hard Margin Type to roughly classify the initial set. A recursive re-clustering approach is proposed for achieving higher degree of relevance in the final clustered set by incorporating ENNPI algorithm. An overall enriched F-Measure value of 94.09% is achieved as compared to existing algorithms.

Item Type: Article
Uncontrolled Keywords: Clustering, Constraint Learning, Normalized Pointwise Mutual Information, Recursive Re-Clustering, SVM
Subjects: Faculty of Engineering > Computer Science & Information Science Engineering
Divisions: University Visvesvarayya College of Engineering > Department of Computer Science and Information Science Engineering
Depositing User: Ms Laxmi Kamble
Date Deposited: 16 Sep 2021 07:09
Last Modified: 16 Sep 2021 07:09
URI: http://eprints-bangaloreuniversity.in/id/eprint/9602

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