Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Sets and Systems: Theory and Applications
Application of majority voting to pattern recognition: an analysis of its behavior and performance
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
A Mixed Ensemble Approach for the Semi-supervised Problem
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Meta methods for model sharing in personal information systems
ACM Transactions on Information Systems (TOIS)
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In this paper we present a voting scheme for cluster algorithms. This voting method allows us to combine several runs of cluster algorithms resulting in a common partition. This helps us to tackle the problem of choosing the appropriate clustering method for a data set where we have no a priori information about it, and to overcome the problems of choosing an optimal result between different repetitions of the same method. Further on, we can improve the ability of a cluster algorithm to find structures in a data set and to validate the resulting partition.