The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Genetic algorithm with deterministic crossover for vector quantization
Pattern Recognition Letters
Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
A novel genetic algorithm for automatic clustering
Pattern Recognition Letters
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classifier hierarchy learning by means of genetic algorithms
Pattern Recognition Letters
To combine steady-state genetic algorithm and ensemble learning for data clustering
Pattern Recognition Letters
Nearest prototype classification: clustering, genetic algorithms, or random search?
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Clustering can be visualized as a grouping problem as it consists of identifying finite set of groups in a dataset. Grouping genetic algorithms are specially designed to handle grouping problems. As the clustering criteria such as minimizing the with-in cluster distance is high-dimensional, non-linear and multi-modal, many standard algorithms available in the literature for clustering tend to converge to a locally optimal solution and/or have slow convergence. Even genetic guided clustering algorithms which are capable of identifying better quality solutions in general are also not totally immune to these shortcomings because of their ad hoc approach towards clustering invalidity and context insensitivity. To remove these shortcomings we have proposed a hybrid steady-state grouping genetic algorithm. Computational results show the effectiveness of our approach.