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 in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
A novel genetic algorithm for automatic clustering
Pattern Recognition Letters
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Fast and Efficient Ensemble Clustering Method
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Classifier hierarchy learning by means of genetic algorithms
Pattern Recognition Letters
A new representation and operators for genetic algorithms applied to grouping problems
Evolutionary Computation
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
Learning assignment order of instances for the constrained K-means clustering algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Construction of the ensemble of logical models in cluster analysis
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
A review: accuracy optimization in clustering ensembles using genetic algorithms
Artificial Intelligence Review
A latent variable pairwise classification model of a clustering ensemble
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Grouping genetic algorithm for data clustering
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
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This paper proposes a data clustering algorithm that combines the steady-state genetic algorithm and the ensemble learning method, termed as genetic-guided clustering algorithm with ensemble learning operator (GCEL). GCEL adopts the steady-state genetic algorithm to perform the search task, but replaces its traditional recombination operator with an ensemble learning operator. Therefore, GCEL can avoid the problems of clustering invalidity and context insensitivity of the traditional recombination operator of genetic algorithms. In addition, GCEL generates its initial population of candidate clustering solutions by using the random subspaces method. Therefore, less fitness evaluations are required to converge. The proposed GCEL is tested on one synthetic and several real data sets. Experimental results demonstrate that GCEL is able to achieve a comparative or better clustering solution with less fitness evaluations when compared with several other existing genetic-guided clustering algorithms.