ACM Computing Surveys (CSUR)
On Clustering Validation Techniques
Journal of Intelligent Information Systems
An improved algorithm for clustering gene expression data
Bioinformatics
Multiobjective genetic algorithm-based fuzzy clustering of categorical attributes
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exploiting the trade-off — the benefits of multiple objectives in data clustering
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
An Evolutionary Approach to Multiobjective Clustering
IEEE Transactions on Evolutionary Computation
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We consider the choice of clustering criteria for use in multiobjective data clustering. We evaluate four different pairs of criteria, three employed in recent evolutionary algorithms for multiobjective clustering, and one from Delattre and Hansen's seminal exact bicriterion method. The criteria pairs are tested here within a single multiobjective evolutionary algorithm and representation scheme to isolate their effects from other considerations. Results on a range of data sets reveal significant performance differences, which can be understood in relation to certain types of challenging cluster structure, and the mathematical form of the criteria. A performance advantage is generally found for those methods that make limited use of cluster centroids and assess partitionings based on aggregate measures of the location of all data points.