Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
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
Learning spatially variant dissimilarity (SVaD) measures
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning spatially variant dissimilarity (SVaD) measures
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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
Locally adaptive metrics for clustering high dimensional data
Data Mining and Knowledge Discovery
An efficient hybrid data clustering method based on K-harmonic means and Particle Swarm Optimization
Expert Systems with Applications: An International Journal
Hyperspherical possibilistic fuzzy c-means for high-dimensional data clustering
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
A novel hybrid K-harmonic means and gravitational search algorithm approach for clustering
Expert Systems with Applications: An International Journal
Research of fast SOM clustering for text information
Expert Systems with Applications: An International Journal
Data clustering based on an efficient hybrid of K-harmonic means, PSO and GA
Transactions on computational collective intelligence IV
International Journal of Intelligent Systems
Fuzzy semi-supervised co-clustering for text documents
Fuzzy Sets and Systems
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Fuzzy co-clustering is a technique that performs simultaneous fuzzy clustering of objects and features. It is known to be suitable for categorizing high-dimensional data, due to its dynamic dimensionality reduction mechanism achieved through simultaneous feature clustering. We introduce a new fuzzy co-clustering algorithm called Heuristic Fuzzy Co-clustering with the Ruspini's condition (HFCR), which addresses several issues in some prominent existing fuzzy co-clustering algorithms. Among these issues are the performance on data sets with overlapping feature clusters and the unnatural representation of feature clusters. The key idea behind HFCR is the formulation of the dual-partitioning approach for fuzzy co-clustering, replacing the existing partitioning-ranking approach. HFCR adopts an efficient and practical heuristic method that can be shown to be more robust than our earlier effort for the dual-partitioning approach. We explain the proposed algorithm in details and provide an analytical study on its advantages. Experimental results on 10 large benchmark document data sets confirm the effectiveness of the new algorithm.