ENHANCED NEIGHBORHOOD NORMALIZED POINTWISE MUTUAL INFORMATION ALGORITHM FOR CONSTRAINT AWARE DATA CLUSTERING

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...

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Bibliographic Details
Published in:ICTACT journal on soft computing 2016-07, Vol.6 (4), p.1287-1292
Main Authors: C N, Pushpa, Deepak, Gerard, Zakir, Mohammed, J, Thriveni, K R, Venugopal
Format: Article
Language:English
Subjects:
Clustering
Constraint Learning
Normalized Pointwise Mutual Information
Recursive Re-Clustering
SVM
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Summary: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.
ISSN:0976-6561
2229-6956
DOI:10.21917/ijsc.2016.0176