Fast and effective text mining using linear-time document clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
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
A new fuzzy relational clustering algorithm based on the fuzzy C-means algorithm
Soft Computing - A Fusion of Foundations, Methodologies and Applications
On relational possibilistic clustering
Pattern Recognition
A method of relational fuzzy clustering based on producing feature vectors using FastMap
Information Sciences: an International Journal
Dual fuzzy-possibilistic coclustering for categorization of documents
IEEE Transactions on Fuzzy Systems
Fuzzy clustering with weighted medoids for relational data
Pattern Recognition
Low-complexity fuzzy relational clustering algorithms for Web mining
IEEE Transactions on Fuzzy Systems
Robust fuzzy clustering of relational data
IEEE Transactions on Fuzzy Systems
A Possibilistic Fuzzy c-Means Clustering Algorithm
IEEE Transactions on Fuzzy Systems
A Novel Similarity-Based Fuzzy Clustering Algorithm by Integrating PCM and Mountain Method
IEEE Transactions on Fuzzy Systems
A multivariate fuzzy c-means method
Applied Soft Computing
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Medoid-based fuzzy clustering generates clusters of objects based on relational data, which records pairwise similarities or dissimilarities among objects. Compared with single-medoid based approaches, our recently proposed fuzzy clustering with multiple-weighted medoids has shown superior performance in clustering via experimental study. In this paper, we present a new version of fuzzy relational clustering in this family called fuzzy clustering with multi-medoids (FMMdd). Based on the new objective function of FMMdd, update equations can be derived more conveniently. Moreover, a unified view of FMMdd and two existing fuzzy relational approaches fuzzy c-medoids (FCMdd) and assignment-prototype (A-P) can be established, which allows us to conduct further analytical study to investigate the effectiveness and feasibility of the proposed approach as well as the limitations of existing ones. The robustness of FMMdd is also investigated. Our theoretical and numerical studies show that the proposed approach produces good quality of clusters with rich cluster-based information and it is less sensitive to noise.