FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Fuzzy PCA-guided robust k-means clustering
IEEE Transactions on Fuzzy Systems
Computers and Industrial Engineering
I-fuzzy partitions for representing clustering uncertainties
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
International Journal of Knowledge Engineering and Soft Data Paradigms
Algorithms in sequential fuzzy regression models based on least absolute deviations
MDAI'10 Proceedings of the 7th international conference on Modeling decisions for artificial intelligence
MDAI'10 Proceedings of the 7th international conference on Modeling decisions for artificial intelligence
Non-hierarchical clustering of decision tables toward rough set-based group decision aid
MDAI'10 Proceedings of the 7th international conference on Modeling decisions for artificial intelligence
Membership enhancement with exponential fuzzy clustering for collaborative filtering
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Semi-supervised agglomerative hierarchical clustering with ward method using clusterwise tolerance
MDAI'11 Proceedings of the 8th international conference on Modeling decisions for artificial intelligence
Two classes of algorithms for data clustering
IUKM'11 Proceedings of the 2011 international conference on Integrated uncertainty in knowledge modelling and decision making
On a comparison between Mahalanobis distance and Choquet integral: The Choquet-Mahalanobis operator
Information Sciences: an International Journal
Alternative fuzzy c-lines and local principal component extraction
International Journal of Knowledge Engineering and Soft Data Paradigms
On pre-processing algorithms for data stream
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
On fuzzy clustering of data streams with concept drift
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
Fuzzy neural gas for unsupervised vector quantization
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
MDAI'12 Proceedings of the 9th international conference on Modeling Decisions for Artificial Intelligence
Inductive clustering and twofold approximations in nearest neighbor clustering
MDAI'12 Proceedings of the 9th international conference on Modeling Decisions for Artificial Intelligence
Fuzzy Cluster Validation Based on Fuzzy PCA-Guided Procedure
International Journal of Fuzzy System Applications
Random walk distances in data clustering and applications
Advances in Data Analysis and Classification
Advanced Engineering Informatics
Relative entropy fuzzy c-means clustering
Information Sciences: an International Journal
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The main subject of this book is the fuzzy c-means proposed by Dunn and Bezdek and their variations including recent studies. A main reason why we concentrate on fuzzy c-means is that most methodology and application studies in fuzzy clustering use fuzzy c-means, and hence fuzzy c-means should be considered to be a major technique of clustering in general, regardless whether one is interested in fuzzy methods or not. Unlike most studies in fuzzy c-means, what we emphasize in this book is a family of algorithms using entropy or entropy-regularized methods which are less known, but we consider the entropy-based method to be another useful method of fuzzy c-means. Throughout this book one of our intentions is to uncover theoretical and methodological differences between the Dunn and Bezdek traditional method and the entropy-based method. We do note claim that the entropy-based method is better than the traditional method, but we believe that the methods of fuzzy c-means become complete by adding the entropy-based method to the method by Dunn and Bezdek, since we can observe natures of the both methods more deeply by contrasting these two.