Semisupervised learning from different information sources
Knowledge and Information Systems
Semi-supervised graph clustering: a kernel approach
ICML '05 Proceedings of the 22nd international conference on Machine learning
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Clustering
Automated constraint selection for semi-supervised clustering algorithm
CAEPIA'09 Proceedings of the Current topics in artificial intelligence, and 13th conference on Spanish association for artificial intelligence
An adaptive kernel method for semi-supervised clustering
ECML'06 Proceedings of the 17th European conference on Machine Learning
Active learning with irrelevant examples
ECML'06 Proceedings of the 17th European conference on Machine Learning
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Semi-supervised clustering aims at accomplishing the clustering task by considering also labels or constraints provided by an external agent. Usually, the agent would provide the output label for a reduced number of patterns or, in the case of lack of posterior information about labels, some pairwise constraints indicating whether or not two patterns should be joined in the same cluster. Constraints may be inferred from some ad-hoc information from sampling, such as their geographical location, which are not directly considered as an input atribute. The objective is to accomplish the clustering task by considering also the pairwise constraints. In this paper we extend the previous work of Yan et al. [10] by obtaining derivative expressions for sigmoidal and polinomial kernels in order to accomplish kernel-clustering semi-supervised tasks. The resulting kernel-clustering task is optimized in relation to kernel parameters which do not need to be provided in advance like in most kernel-clustering tasks. Instead, kernel parameters are obtained as the outcome of the optimization problem.