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

Autor:	C.N. Pushpa, Gerard Deepak, Mohammed Zakir, J Thriveni, K.R. Venugopal
Jazyk:	angličtina
Rok vydání:	2016
Předmět:	Clustering Constraint Learning Normalized Pointwise Mutual Information Recursive Re-Clustering SVM Computer engineering. Computer hardware TK7885-7895
Zdroj:	ICTACT Journal on Soft Computing, Vol 6, Iss 4, Pp 1287-1292 (2016)
Druh dokumentu:	article
ISSN:	0976-6561 2229-6956
Popis:	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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/fafefefbd9ba4f9badce02d7a6f100e5 Zobrazit plný text záznamu View record in DOAJ