article

Possibilistic fuzzy co-clustering of large document collections

Authors:

William-Chandra Tjhi,

Lihui ChenAuthors Info & Claims

Pattern Recognition, Volume 40, Issue 12

Pages 3452 - 3466

https://doi.org/10.1016/j.patcog.2007.04.017

Published: 01 December 2007 Publication History

Abstract

In this paper we propose a new co-clustering algorithm called possibilistic fuzzy co-clustering (PFCC) for automatic categorization of large document collections. PFCC integrates a possibilistic document clustering technique and a combined formulation of fuzzy word ranking and partitioning into a fast iterative co-clustering procedure. This novel framework brings about simultaneously some benefits including robustness in the presence of document and word outliers, rich representations of co-clusters, highly descriptive document clusters, a good performance in a high-dimensional space, and a reduced sensitivity to the initialization in the possibilistic clustering. We present the detailed formulation of PFCC together with the explanations of the motivations behind. The advantages over other existing works and the algorithm's proof of convergence are provided. Experiments on several large document data sets demonstrate the effectiveness of PFCC.

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Possibilistic fuzzy co-clustering of large document collections

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Published In

cover image Pattern Recognition

Pattern Recognition Volume 40, Issue 12

December, 2007

441 pages

ISSN:0031-3203

Issue’s Table of Contents

Copyright © Pattern Recognition Society © 2007.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 December 2007

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Cited By

Wang HSong YChen WLuo ZLi CLi T(2024)A Survey of Co-ClusteringACM Transactions on Knowledge Discovery from Data10.1145/368179318:9(1-28)Online publication date: 25-Jul-2024
https://dl.acm.org/doi/10.1145/3681793
Pham VNgo LNguyen T(2017)Feature-reduction fuzzy co-clustering algorithm for hyperspectral image segmentation2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)10.1109/FUZZ-IEEE.2017.8015643(1-6)Online publication date: 9-Jul-2017
https://dl.acm.org/doi/10.1109/FUZZ-IEEE.2017.8015643
Zhou JWang QHung CYang F(2017)Credibilistic clustering algorithms via alternating cluster estimationJournal of Intelligent Manufacturing10.1007/s10845-014-1004-628:3(727-738)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1007/s10845-014-1004-6
Kannan SDevi RRamathilagam SHong T(2017)Effective fuzzy possibilistic c-meansSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-016-2198-721:11(2835-2845)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1007/s00500-016-2198-7
Pham VNgo LPedrycz W(2016)Interval-valued fuzzy set approach to fuzzy co-clustering for data classificationKnowledge-Based Systems10.1016/j.knosys.2016.05.049107:C(1-13)Online publication date: 1-Sep-2016
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Shi KLi L(2013)High performance genetic algorithm based text clustering using parts of speech and outlier eliminationApplied Intelligence10.1007/s10489-012-0382-838:4(511-519)Online publication date: 1-Jun-2013
https://dl.acm.org/doi/10.1007/s10489-012-0382-8
Yan YChen LTjhi W(2013)Semi-supervised fuzzy co-clustering algorithm for document categorizationKnowledge and Information Systems10.1007/s10115-011-0454-934:1(55-74)Online publication date: 1-Jan-2013
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Van Nha Pham Ngo L(undefined)Interval type-2 fuzzy co-clustering algorithm2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)10.1109/FUZZ-IEEE.2015.7337960(1-7)
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