On the monotonicity of hierarchical sum--product fuzzy systems
Fuzzy Sets and Systems
Clustering: A neural network approach
Neural Networks
A new approach to clustering data with arbitrary shapes
Pattern Recognition
A soft hierarchical algorithm for the clustering of multiple bioactive chemical compounds
BIRD'07 Proceedings of the 1st international conference on Bioinformatics research and development
A robust EM clustering algorithm for Gaussian mixture models
Pattern Recognition
A two-leveled symbiotic evolutionary algorithm for clustering problems
Applied Intelligence
Hierarchical tree clustering of fuzzy number
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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A recursive algorithm for hierarchical fuzzy partitioning is presented. The algorithm has the advantages of hierarchical clustering, while maintaining fuzzy clustering rules. Each pattern can have a nonzero membership in more than one subset of the data in the hierarchy. Optimal feature extraction and reduction is optionally reapplied for each subset. Combining hierarchical and fuzzy concepts is suggested as a natural feasible solution to the cluster validity problem of real data. The convergence and membership conservation of the algorithm are proven. The algorithm is shown to be effective for a variety of data sets with a wide dynamic range of both covariance matrices and number of members in each class