Please use this identifier to cite or link to this item: https://ir.iimcal.ac.in:8443/jspui/handle/123456789/1077
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dc.contributor.authorChakrabarti, Muktamala
dc.contributor.authorPal, Asim Kumar
dc.date.accessioned2021-08-26T06:03:24Z-
dc.date.available2021-08-26T06:03:24Z-
dc.date.issued2014
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84911890349&doi=10.1007%2f978-3-319-13560-1&partnerID=40&md5=d2bbc5b8b2c25439729301ea670e66c1
dc.identifier.urihttps://ir.iimcal.ac.in:8443/jspui/handle/123456789/1077-
dc.descriptionChakrabarti, Muktamala, Indian Institute of Management Calcutta, India; Pal, Asim Kumar, Indian Institute of Management Calcutta, India
dc.descriptionISSN/ISBN - 03029743
dc.descriptionpp.370-382
dc.descriptionDOI - 10.1007/978-3-319-13560-1
dc.description.abstractText clustering and constrained clustering both have been an important area of research over the years. The commonly used vector space representation of text data involves high dimensional sparse matrices. We present algorithms which are improvements over the existing algorithms namely, Online Linear Constrained Vector Quantization Error (O-LCVQE) and Constrained Rival Penalized Competitive Learning (C-RPCL). We use the concept of spherical k-means in place of Euclidean distance based traditional k-means. Several experiments demonstrate that the proposed algorithms work better for high dimensional text data in terms of normalized mutual information. We further show that k-means with rival penalized competitive learning is a much better alternative than simple k-means when applied on text data. The performances of k-means and spherical k-means come closer, when the distance function is weighted by the neuron winning frequency. � Springer International Publishing Switzerland 2014.
dc.publisherSCOPUS
dc.publisherLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.publisherSpringer Verlag
dc.relation.ispartofseries8862
dc.subjectC-RPCL
dc.subjectCompetitive learning
dc.subjectConstrained clustering
dc.subjectCOP-kmeans
dc.subjectK-means
dc.subjectO-LCVQE
dc.subjectPairwise constraints
dc.subjectSpherical k-means
dc.titleCompetitive learning with pairwise constraints for text
dc.typeArticle
Appears in Collections:Management Information Systems

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