Semi-supervised clustering with discriminative random fields

Semi-supervised clustering exploits a small quantity of supervised information to improve the accuracy of data clustering. In this paper, a framework for semi-supervised clustering is proposed. This framework is capable of integrating with a traditional clustering algorithm seamlessly, and particula...

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Bibliographic Details
Published in:	Pattern recognition Vol. 45; no. 12; pp. 4402 - 4413
Main Authors:	Chang, Chin-Chun, Chen, Hsin-Yi
Format:	Journal Article
Language:	English
Published:	Kidlington Elsevier Ltd 01.12.2012 Elsevier
Subjects:	Accuracy Algorithms Applied sciences Clustering Consistency Discriminative random fields Exact sciences and technology Feasibility Information, signal and communications theory Learning Pattern recognition Semi-supervised clustering Signal and communications theory Signal representation. Spectral analysis Signal, noise Telecommunications and information theory Discriminative random fields Semi-supervised clustering Discriminant analysis Automatic classification Iterative method Signal classification Supervised classification Random field Accuracy Posterior probability Supervised learning A posteriori estimation Feasibility Metric Learning algorithm
ISSN:	0031-3203, 1873-5142
Online Access:	Get full text
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