A Modified Version of the K-Means Algorithm with a Distance Based on Cluster Symmetry
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
A Modified K-Means Algorithm for Circular Invariant Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A modified k-means clustering with a density-sensitive distance metric
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Clustering with a genetically optimized approach
IEEE Transactions on Evolutionary Computation
An Evolutionary Approach to Multiobjective Clustering
IEEE Transactions on Evolutionary Computation
Natural and remote sensing image segmentation using memetic computing
IEEE Computational Intelligence Magazine
Multi-elitist immune clonal quantum clustering algorithm
Neurocomputing
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In this study, we propose a novel evolutionary algorithm-based clustering method, named density-sensitive evolutionary clustering (DSEC). In DSEC, each individual is a sequence of real integer numbers representing the cluster representatives, and each data item is assigned to a cluster representative according to a novel density-sensitive dissimilarity measure which can measure the geodesic distance along the manifold. DSEC searches the optimal cluster representatives from a combinatorial optimization viewpoint using evolutionary algorithm. The experimental results on seven artificial data sets with different manifold structure show that the novel density-sensitive evolutionary clustering algorithm has the ability to identify complex non-convex clusters compared with the K-Means algorithm, a genetic algorithm-based clustering, and a modified K-Means algorithm with the density-sensitive distance metric.