Please use this identifier to cite or link to this item: https://idr.l1.nitk.ac.in/jspui/handle/123456789/8584
Full metadata record
DC FieldValueLanguage
dc.contributor.authorBhopale, A.P.-
dc.contributor.authorSowmya, Kamath S.-
dc.date.accessioned2020-03-30T10:22:27Z-
dc.date.available2020-03-30T10:22:27Z-
dc.date.issued2017-
dc.identifier.citation2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, 2017, Vol.2017-January, , pp.1471-1477en_US
dc.identifier.urihttps://idr.nitk.ac.in/jspui/handle/123456789/8584-
dc.description.abstractDealing with high dimensional data is a challenging and computationally complex task in the data pre-processing phase of text clustering. Conventionally, union and intersection approaches have been used to combine results of different feature selection methods to optimize relevant feature space for document collection. Union method selects all features from considered sub-models, whereas, intersection method selects only common features identified by sub-models. However, in reality, any type of feature selection can cause a loss of some potentially important features. In this paper, a hybrid feature selection model called Modified Hybrid Union (MHU) is proposed, which selects features by considering the individual strengths and weaknesses of each constituent component of the model. A comparative evaluation of its performance for K-means clustering and Bio-inspired Flockbased clustering is also presented on standard data sets such as OWL-S TC and Reuters-21578. � 2017 IEEE.en_US
dc.titleNovel hybrid feature selection models for unsupervised document categorizationen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

Files in This Item:
File Description SizeFormat 
4 Novel Hybrid Feature.pdf453.16 kBAdobe PDFThumbnail
View/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.