A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
In search of optimal clusters using genetic algorithms
Pattern Recognition Letters
A clustering strategy based on a formalism of the reproductive process in natural systems
SIGIR '79 Proceedings of the 2nd annual international ACM SIGIR conference on Information storage and retrieval: information implications into the eighties
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Efficient automatic engineering design synthesis via evolutionary exploration
Efficient automatic engineering design synthesis via evolutionary exploration
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
GAPS: A clustering method using a new point symmetry-based distance measure
Pattern Recognition
Palmprints: a novel co-evolutionary algorithm for clustering finger images
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Clustering with a genetically optimized approach
IEEE Transactions on Evolutionary Computation
Cooperative co-evolutionary optimization of software project staff assignments and job scheduling
SSBSE'11 Proceedings of the Third international conference on Search based software engineering
Natural vs. unnatural decomposition in cooperative coevolution
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
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A challenge in partitional clustering is determining the number of clusters that best characterize a set of observations. In this paper, we present a novel approach for determining both an optimal number of clusters and partitioning of the data set. Our new algorithm is based on cooperative coevolution and inspired by the natural process of sympatric speciation. We have evaluated our algorithm on a number of synthetic and real data sets from pattern recognition literature and on a recentlycollected set of epigenetic data consisting of DNA methylation levels. In a comparison with a state-of-the-art algorithm that uses a variable string-length GA for clustering, our algorithm demonstrated a significant performance advantage, both in terms of determining an appropriate number of clusters and in the quality of the cluster assignments as reflected by the misclassification rate.