vioft2nntf2t|tblJournal|Abstract_paper|0xf4ffee8e1f000000878f010001000400 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 Pointwise 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.
C.N. Pushpa, Gerard Deepak, Mohammed Zakir, J Thriveni, K.R. Venugopal
University Visvesvaraya College of Engineering, India
Clustering, Constraint Learning, Normalized Pointwise Mutual Information, Recursive Re-Clustering, SVM
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| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 6 , Issue: 4 , Pages: 1287-1292 )
Date of Publication :
July 2016
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126
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