Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Experimental results of randomized clustering algorithm
Proceedings of the twelfth annual symposium on Computational geometry
Self-organizing maps
Context-specific Bayesian clustering for gene expression data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Clustering Algorithms
Probabilistic hierarchical clustering for biological data
Proceedings of the sixth annual international conference on Computational biology
Clustering validity checking methods: part II
ACM SIGMOD Record
Model-based clustering in gene expression microarrays: an application to breast cancer data
APBC '03 Proceedings of the First Asia-Pacific bioinformatics conference on Bioinformatics 2003 - Volume 19
Validating and Refining Clusters via Visual Rendering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
FGKA: a Fast Genetic K-means Clustering Algorithm
Proceedings of the 2004 ACM symposium on Applied computing
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
Resampling Method for Unsupervised Estimation of Cluster Validity
Neural Computation
Multiobjective clustering with automatic k-determination for large-scale data
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Evolutionary spectral clustering by incorporating temporal smoothness
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
An improved algorithm for clustering gene expression data
Bioinformatics
Graph partitioning through a multi-objective evolutionary algorithm: a preliminary study
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Multi-objective genetic algorithms based automated clustering for fuzzy association rules mining
Journal of Intelligent Information Systems
Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II
IEEE Transactions on Evolutionary Computation
A survey of evolutionary algorithms for clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Numerical methods for fuzzy clustering
Information Sciences: an International Journal
A Multiobjective and Evolutionary Clustering Method for Dynamic Networks
ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining
K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality
IEEE Transactions on Pattern Analysis and Machine Intelligence
ADVIS'04 Proceedings of the Third international conference on Advances in Information Systems
Multiobjective Genetic Algorithms for Clustering: Applications in Data Mining and Bioinformatics
Multiobjective Genetic Algorithms for Clustering: Applications in Data Mining and Bioinformatics
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Clustering is an essential research problem which has received considerable attention in the research community for decades. It is a challenge because there is no unique solution that fits all problems and satisfies all applications. We target to get the most appropriate clustering solution for a given application domain. In other words, clustering algorithms in general need prior specification of the number of clusters, and this is hard even for domain experts to estimate especially in a dynamic environment where the data changes and/or become available incrementally. In this paper, we described and analyze the effectiveness of a robust clustering algorithm which integrates multi-objective genetic algorithm into a framework capable of producing alternative clustering solutions; it is called Multi-objective K-Means Genetic Algorithm (MOKGA). We investigate its application for clustering a variety of datasets, including microarray gene expression data. The reported results are promising. Though we concentrate on gene expression and mostly cancer data, the proposed approach is general enough and works equally to cluster other datasets as demonstrated by the two datasets Iris and Ruspini. After running MOKGA, a pareto-optimal front is obtained, and gives the optimal number of clusters as a solution set. The achieved clustering results are then analyzed and validated under several cluster validity techniques proposed in the literature. As a result, the optimal clusters are ranked for each validity index. We apply majority voting to decide on the most appropriate set of validity indexes applicable to every tested dataset. The proposed clustering approach is tested by conducting experiments using seven well cited benchmark data sets. The obtained results are compared with those reported in the literature to demonstrate the applicability and effectiveness of the proposed approach.