Algorithms for clustering data
Algorithms for clustering data
Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
Redundant representations in evolutionary computation
Evolutionary Computation
An evolutionary clustering algorithm for gene expression microarray data analysis
IEEE Transactions on Evolutionary Computation
An Evolutionary Approach to Multiobjective Clustering
IEEE Transactions on Evolutionary Computation
Multiobjective clustering with automatic k-determination for large-scale data
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Towards Dynamic Fitness Based Partitioning for IntraVascular UltraSound Image Analysis
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Evolutionary image segmentation based on multiobjective clustering
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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This paper analyses the properties of four alternative representation/operator combinations suitable for data clustering algorithms that keep the number of clusters variable. These representations are investigated in the context of their performance when used in a multiobjective evolutionary clustering algorithm (MOCK), which we have described previously. To shed light on the resulting performance differences observed, we consider the relative size of the search space and heuristic bias inherent to each representation, as well as its locality and heritability under the associated variation operators. We find that the representation that performs worst when a random initialization is employed, is nevertheless the best overall performer given the heuristic initialization normally used in MOCK. This suggests there are strong interaction effects between initialization, representation and operators in this problem.