Multi-regularization for fuzzy co-clustering

Vikas K. Garg, Sneha Chaudhari, Ankur Narang

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)


Co-clustering is a powerful technique with varied applications in text clustering and recommender systems. For large scale high dimensional and sparse real world data, there is a strong need to provide an overlapped co-clustering algorithm that mitigates the effect of noise and non-discriminative information, generalizes well to the unseen data, and performs well with respect to several quality measures. In this paper, we introduce a novel fuzzy co-clustering algorithm that incorporates multiple regularizers to address these important issues. Specifically, we propose MRegFC that considers terms corresponding to Entropy, Gini Index, and Joint Entropy simultaneously. We demonstrate that MRegFC generates significantly higher quality results compared to many existing approaches on several real world benchmark datasets.

Original languageEnglish
Title of host publicationNeural Information Processing - 20th International Conference, ICONIP 2013, Proceedings
Number of pages9
EditionPART 2
Publication statusPublished - 2013
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Neural Information Processing - Daegu, Korea, Republic of
Duration: 3 Nov 20137 Nov 2013
Conference number: 20

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume8227 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Neural Information Processing
Abbreviated titleICONIP
Country/TerritoryKorea, Republic of


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