Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
A probabilistic approach to clustering
Pattern Recognition Letters
Artificial Intelligence
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Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
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ACM Computing Surveys (CSUR)
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
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ACM SIGMOD Record
Clustering validity checking methods: part II
ACM SIGMOD Record
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Online clustering of parallel data streams
Data & Knowledge Engineering
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IEEE Transactions on Computers
ECM: An evidential version of the fuzzy c-means algorithm
Pattern Recognition
Dynamic data assigning assessment clustering of streaming data
Applied Soft Computing
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Fuzzy Sets and Systems
International Journal of Approximate Reasoning
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AICCSA '10 Proceedings of the ACS/IEEE International Conference on Computer Systems and Applications - AICCSA 2010
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
CECM: Constrained evidential C-means algorithm
Computational Statistics & Data Analysis
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MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
An approach to online identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Data stream clustering: A survey
ACM Computing Surveys (CSUR)
An extension to Rough c-means clustering based on decision-theoretic Rough Sets model
International Journal of Approximate Reasoning
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A new online clustering method called E2GK (Evidential Evolving Gustafson-Kessel) is introduced. This partitional clustering algorithm is based on the concept of credal partition defined in the theoretical framework of belief functions. A credal partition is derived online by applying an algorithm resulting from the adaptation of the Evolving Gustafson-Kessel (EGK) algorithm. Online partitioning of data streams is then possible with a meaningful interpretation of the data structure. A comparative study with the original online procedure shows that E2GK outperforms EGK on different entry data sets. To show the performance of E2GK, several experiments have been conducted on synthetic data sets as well as on data collected from a real application problem. A study of parameters' sensitivity is also carried out and solutions are proposed to limit complexity issues.