A Framework for Semi-Supervised Learning Based on Subjective and Objective Clustering Criteria

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In this paper, we propose a semi-supervised framework for learning a weighted Euclidean subspace, where the best clustering can be achieved. Our approach capitalizes on user-constraints and the quality of intermediate clustering results in terms of its structural properties. It uses the clustering algorithm and the validity measure as parameters.

Maria Halkidi, Dimitrios Gunopulos, Nitin Kumar, M

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Data Mining | ICDM 2005 | Semi-supervised Framework | Weighted Euclidean Subspace |

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Added	24 Jun 2010
Updated	24 Jun 2010
Type	Conference
Year	2005
Where	ICDM
Authors	Maria Halkidi, Dimitrios Gunopulos, Nitin Kumar, Michalis Vazirgiannis, Carlotta Domeniconi

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A Framework for Semi-Supervised Learning Based on Subjective and Objective Clustering Criteria

Data Mining | ICDM 2005 | Semi-supervised Framework | Weighted Euclidean Subspace |

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