ACM Computing Surveys (CSUR)
An empirical comparison of four initialization methods for the K-Means algorithm
Pattern Recognition Letters
Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
Information Retrieval
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Clustering Ensembles: Models of Consensus and Weak Partitions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evolutionary model selection in unsupervised learning
Intelligent Data Analysis
Multiobjective data clustering
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Approximating the Knee of an MOP with Stochastic Search Algorithms
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Using Cluster Analysis to Improve the Design of Component Interfaces
ASE '08 Proceedings of the 2008 23rd IEEE/ACM International Conference on Automated Software Engineering
Evolutionary multi-objective clustering for overlapping clusters detection
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A multiobjectivization approach for vehicle routing problems
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
International Journal of Data Mining and Bioinformatics
A review: accuracy optimization in clustering ensembles using genetic algorithms
Artificial Intelligence Review
Multiobjective optimization of co-clustering ensembles
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Clustering criteria in multiobjective data clustering
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
International Journal of Hybrid Intelligent Systems
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In previous work, we have proposed a novel approach to data clustering based on the explicit optimization of a partitioning with respect to two complementary clustering objectives [6]. Here, we extend this idea by describing an advanced multiobjective clustering algorithm, MOCK, with the capacity to identify good solutions from the Pareto front, and to automatically determine the number of clusters in a data set. The algorithm has been subject to a thorough comparison with alternative clustering techniques and we briefly summarize these results. We then present investigations into the mechanisms at the heart of MOCK: we discuss a simple example demonstrating the synergistic effects at work in multiobjective clustering, which explain its superiority to single-objective clustering techniques, and we analyse how MOCK's Pareto fronts compare to the performance curves obtained by single-objective algorithms run with a range of different numbers of clusters specified.