Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
A survey of constrained classification
Computational Statistics & Data Analysis
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Semi-supervised Clustering by Seeding
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Gene functional classification by semi-supervised learning from heterogeneous data
Proceedings of the 2003 ACM symposium on Applied computing
Multiobjective Optimization in Bioinformatics and Computational Biology
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Agglomerative genetic algorithm for clustering in social networks
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Semi-GAPS: A Semi-supervised Clustering Method Using Point Symmetry
Fundamenta Informaticae
Collaborative clustering with background knowledge
Data & Knowledge Engineering
Multi-objective clustering ensemble with prior knowledge
BSB'07 Proceedings of the 2nd Brazilian conference on Advances in bioinformatics and computational biology
An enriched game-theoretic framework for multi-objective clustering
Applied Soft Computing
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Semi-supervised classification uses aspects of both unsupervised and supervised learning to improve upon the performance of traditional classification methods. Semi-supervised clustering, in particular, explicitly integrates both information about the data distribution and about class memberships into the clustering process. In this paper, the potential of a multiobjective formulation of the semi-supervised clustering problem is explored, and two evolutionary multiobjective approaches to the problem are outlined. Experimental results demonstrate practical performance benefits of this methodology, including an improved classification performance and an increased robustness towards annotation errors.