Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Knowledge-Based Clustering: From Data to Information Granules
Knowledge-Based Clustering: From Data to Information Granules
Unsupervised possibilistic clustering
Pattern Recognition
Quantum-inspired evolutionary clustering algorithm based on manifold distance
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Metaheuristic Clustering
A quantum-inspired genetic algorithm for k-means clustering
Expert Systems with Applications: An International Journal
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Automatic Clustering Using an Improved Differential Evolution Algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A possibilistic approach to clustering
IEEE Transactions on Fuzzy Systems
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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In previous work, a novel approach to data clustering based on quantum evolutionary algorithm has been proposed. In a comparison to other evolutionary clustering algorithms, the approach showed a high performance in terms of effectiveness and quality of found clusters. Although the approach is sound, it tends to be trapped in local minima, which slows the convergence. The approach is based on degrees of belonging having a fixed relationship with the distance between the data points and the clusters. The fixed relationship ignores completely the dataset distribution. In this paper, we modify the approach to improve its convergence. We also modify the function calculating the degrees of belonging by taking inspiration from possibilistic clustering. Comparison has been done with approaches based on degrees of belonging like fuzzy, possibilistic, hybrid fuzzy possibilistic clustering and other quantum evolutionary algorithm. Results on both real and synthetic datasets show that the modifications brought to the approach enable a more efficient exploration of the search space which improves the convergence speed and quality.