Fuzzy clustering with squared Minkowski distances
Fuzzy Sets and Systems - Special issue on clustering and learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A survey of evolutionary algorithms for clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Clustering with a genetically optimized approach
IEEE Transactions on Evolutionary Computation
An Evolutionary Approach to Multiobjective Clustering
IEEE Transactions on Evolutionary Computation
Analysis of the weighting exponent in the FCM
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Method of Face Recognition Based on Fuzzy c-Means Clustering and Associated Sub-NNs
IEEE Transactions on Neural Networks
On Efficient Learning Machine With Root-Power Mean Neuron in Complex Domain
IEEE Transactions on Neural Networks
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In this paper, we present a novel evolutionary fuzzy clustering approach with Minkowski distances. Fuzzy clustering plays an important role for various kinds of classification problems. Evolutionary algorithm is used for searching the best partitioning among the populations generated by different runs of the fuzzy clustering algorithm. Evolutionary fuzzy clustering performs better as compared to the conventional fuzzy clustering in terms of classification accuracy and partitioning. Fuzzy c-means (FCM) is a data clustering algorithm in which each data point is associated with a cluster through a membership degree. Here, Minkowski distance is used with FCM instead of conventional Euclidian distance because of its more generalized nature. It does not restrict the shape of the clusters generated. Empirical evaluation demonstrates the performance of proposed novel technique in terms of precision and accuracy in various benchmark problems.