Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Characterization and detection of noise in clustering
Pattern Recognition Letters
A robust algorithm for automatic extraction of an unknown number of clusters from noisy data
Pattern Recognition Letters
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Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Three-way fuzzy clustering models for LR fuzzy time trajectories
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
A weighted fuzzy c-means clustering model for fuzzy data
Computational Statistics & Data Analysis
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
Robust clustering methods: a unified view
IEEE Transactions on Fuzzy Systems
Optimization of clustering criteria by reformulation
IEEE Transactions on Fuzzy Systems
Research on Personalized E-Learning System Using Fuzzy Set Based Clustering Algorithm
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
Fuzzy cluster stability analysis with missing values using resampling
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Computational Statistics & Data Analysis
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Expert Systems with Applications: An International Journal
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Pattern Recognition
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Fuzzy clusterwise quasi-likelihood generalized linear models
Advances in Data Analysis and Classification
Expert Systems with Applications: An International Journal
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Information Sciences: an International Journal
CECM: Constrained evidential C-means algorithm
Computational Statistics & Data Analysis
Learning partial ordinal class memberships with kernel-based proportional odds models
Computational Statistics & Data Analysis
A constructive particle swarm algorithm for fuzzy clustering
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
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An encompassing, self-contained introduction to the foundations of the broad field of fuzzy clustering is presented. The fuzzy cluster partitions are introduced with special emphasis on the interpretation of the two most encountered types of gradual cluster assignments: the fuzzy and the possibilistic membership degrees. A systematic overview of present fuzzy clustering methods is provided, highlighting the underlying ideas of the different approaches. The class of objective function-based methods, the family of alternating cluster estimation algorithms, and the fuzzy maximum likelihood estimation scheme are discussed. The latter is a fuzzy relative of the well-known expectation maximization algorithm and it is compared to its counterpart in statistical clustering. Related issues are considered, concluding with references to selected developments in the area.