Algorithms for clustering data
Algorithms for clustering data
Technical note: some properties of splitting criteria
Machine Learning
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Theoretical Comparison between the Gini Index and Information Gain Criteria
Annals of Mathematics and Artificial Intelligence
Solving cluster ensemble problems by bipartite graph partitioning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Data Mining In Bioinformatics (Advanced Information and Knowledge Processing)
Data Mining In Bioinformatics (Advanced Information and Knowledge Processing)
Intelligent Bioinformatics: The Application of Artificial Intelligence Techniques to Bioinformatics Problems
On semi-supervised clustering via multiobjective optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Multi-Objective Clustering Ensemble
HIS '06 Proceedings of the Sixth International Conference on Hybrid Intelligent Systems
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Multiobjective data clustering
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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
Survey of clustering algorithms
IEEE Transactions on Neural Networks
A review: accuracy optimization in clustering ensembles using genetic algorithms
Artificial Intelligence Review
An enriched game-theoretic framework for multi-objective clustering
Applied Soft Computing
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In this paper, we introduce an approach to integrate prior knowledge in cluster analysis, which is different from the existing ones for semi-supervised clustering methods. In order to aid the discovery of alternative structures present in the data, we consider the knowledge of some existing complete classification of such data. The approach proposed is based on our Multi-Objective Clustering Ensemble algorithm (MOCLE). This algorithm generates a concise and stable set of partitions, which represents different trade-offs between several measures of partition quality. The prior knowledge is automatically integrated in MOCLE by embedding it into one of the objective functions. In this case, the function gives as output the quality of a partition, considering the prior knowledge of one of the known structures of the data.