A near-optimal initial seed value selection in K-means algorithm using a genetic algorithm
Pattern Recognition Letters
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Iterative shrinking method for clustering problems
Pattern Recognition
A survey of evolutionary algorithms for clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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The K-means algorithm is one of the most widely used clustering methods. However, solutions obtained by it are strongly dependent on initialization of cluster centers. In the paper a novel genetic algorithm, called GAKMI (Genetic Algorithm for the K-Means Initialization), for the selection of initial cluster centers is proposed. Contrary to most of the approaches described in the literature, which encode coordinates of cluster centers directly in a chromosome, our method uses binary encoding. In this encoding bits set to one select elements of the learning set as initial cluster centers. Since in our approach not every binary chromosome encodes a feasible solution, we propose two repair algorithms to convert infeasible chromosomes into feasible ones. GAKMI was tested on three datasets, using varying number of clusters. The experimental results are encouraging.