Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Data mining: concepts and techniques
Data mining: concepts and techniques
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Data Mining for Scientific and Engineering Applications
Data Mining for Scientific and Engineering Applications
A matrix density based algorithm to hierarchically co-cluster documents and words
WWW '03 Proceedings of the 12th international conference on World Wide Web
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Document clustering by concept factorization
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Document clustering via adaptive subspace iteration
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
The indexable web is more than 11.5 billion pages
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
General C-Means Clustering Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Co-clustering by block value decomposition
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A partitioning based algorithm to fuzzy co-cluster documents and words
Pattern Recognition Letters
A new fuzzy co-clustering algorithm for categorization of datasets with overlapping clusters
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
The possibilistic C-means algorithm: insights and recommendations
IEEE Transactions on Fuzzy Systems
Comments on “A possibilistic approach to clustering”
IEEE Transactions on Fuzzy Systems
Improved possibilistic C-means clustering algorithms
IEEE Transactions on Fuzzy Systems
A Possibilistic Fuzzy c-Means Clustering Algorithm
IEEE Transactions on Fuzzy Systems
A New Convergence Proof of Fuzzy c-Means
IEEE Transactions on Fuzzy Systems
A possibilistic approach to clustering
IEEE Transactions on Fuzzy Systems
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Using a new relational concept to improve the clustering performance of search engines
Information Processing and Management: an International Journal
Fuzzy and possibilistic clustering for fuzzy data
Computational Statistics & Data Analysis
On possibilistic clustering with repulsion constraints for imprecise data
Information Sciences: an International Journal
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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.