Algorithms for clustering data
Algorithms for clustering data
Cluster validity methods: part I
ACM SIGMOD Record
Why so many clustering algorithms: a position paper
ACM SIGKDD Explorations Newsletter
Clustering validity checking methods: part II
ACM SIGMOD Record
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Solving cluster ensemble problems by bipartite graph partitioning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Open source clustering software
Bioinformatics
Multi-Objective Clustering Ensemble
HIS '06 Proceedings of the Sixth International Conference on Hybrid Intelligent Systems
Multiobjective data clustering
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
An Evolutionary Approach to Multiobjective Clustering
IEEE Transactions on Evolutionary Computation
A Framework for Multi-Objective Clustering and Its Application to Co-Location Mining
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
A survey of evolutionary algorithms for clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
International Journal of Hybrid Intelligent Systems
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In this paper we present an algorithm for cluster analysis that integrates aspects from cluster ensemble and multi-objective clustering. The algorithm is constituted by a Pareto-based multi-objective genetic algorithm that uses clustering validation measures as the objective functions. This algorithm also uses a special consensus crossover operator. The proposed algorithm can deal with datasets with different types of clusters, without the need of much expertise in cluster analysis and the application domain. Moreover, it results in a concise and stable set of partitions representing different trade-offs between two validation measures related to different clustering criteria.