A novel grammar-based genetic programming approach to clustering
Proceedings of the 2005 ACM symposium on Applied computing
An evolutionary data clustering algorithm
ICCOMP'07 Proceedings of the 11th WSEAS International Conference on Computers
Differential evolution and particle swarm optimisation in partitional clustering
Computational Statistics & Data Analysis
Mining comprehensible clustering rules with an evolutionary algorithm
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Multi-objective Genetic Algorithms for grouping problems
Applied Intelligence
Unsupervised learning using multivariate symbolic hybrid
CBMS'03 Proceedings of the 16th IEEE conference on Computer-based medical systems
PSO aided k-means clustering: introducing connectivity in k-means
Proceedings of the 13th annual conference on Genetic and evolutionary computation
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
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Clustering is a hard combinatorial problem and is defined as the unsupervised classification of patterns. The formation of clusters is based on the principle of maximizing the similarity between objects of the same cluster while simultaneously minimizing the similarity between objects belonging to distinct clusters. This paper presents a tool for database clustering using a rule-based genetic algorithm (RBCGA). RBCGA evolves individuals consisting of a fixed set of clustering rules, where each rule includes d non-binary intervals, one for each feature. The investigations attempt to alleviate certain drawbacks related to the classical minimization of square-error criterion by suggesting a flexible fitness function which takes into consideration, cluster asymmetry, density, coverage and homogeny.