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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Vydané v:	Pattern recognition Ročník 45; číslo 12; s. 4402 - 4413
Hlavní autori:	Chang, Chin-Chun, Chen, Hsin-Yi
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Kidlington Elsevier Ltd 01.12.2012 Elsevier
Predmet:	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
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Abstract	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 particularly useful for the application where a traditional clustering is designated to use. In the proposed framework, discriminative random fields (DRFs) are employed to model the consistency between the result of a traditional clustering algorithm and the supervised information with the assumption of semi-supervised learning. The semi-supervised clustering problem is thus formulated as finding the label configuration with the maximum a posteriori (MAP) probability of the DRF. A procedure based on the iterated conditional modes algorithm and a metric-learning algorithm is developed to find a suboptimal MAP solution of the DRF. The proposed approach has been tested against various data sets. Experimental results demonstrate that our approach can enhance the clustering accuracy, and thus prove the feasibility of the proposed approach. ► We plan a framework capable of integrating with a traditional clustering algorithm seamlessly for semi-supervised clustering. ► We find that discriminative random fields are useful for semi-supervised clustering. ► The proposed framework is a hybrid approach, which makes better use of supervised information for semi-supervised clustering.
AbstractList	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 particularly useful for the application where a traditional clustering is designated to use. In the proposed framework, discriminative random fields (DRFs) are employed to model the consistency between the result of a traditional clustering algorithm and the supervised information with the assumption of semi-supervised learning. The semi-supervised clustering problem is thus formulated as finding the label configuration with the maximum a posteriori (MAP) probability of the DRF. A procedure based on the iterated conditional modes algorithm and a metric-learning algorithm is developed to find a suboptimal MAP solution of the DRF. The proposed approach has been tested against various data sets. Experimental results demonstrate that our approach can enhance the clustering accuracy, and thus prove the feasibility of the proposed approach. ► We plan a framework capable of integrating with a traditional clustering algorithm seamlessly for semi-supervised clustering. ► We find that discriminative random fields are useful for semi-supervised clustering. ► The proposed framework is a hybrid approach, which makes better use of supervised information for semi-supervised clustering. 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 particularly useful for the application where a traditional clustering is designated to use. In the proposed framework, discriminative random fields (DRFs) are employed to model the consistency between the result of a traditional clustering algorithm and the supervised information with the assumption of semi-supervised learning. The semi-supervised clustering problem is thus formulated as finding the label configuration with the maximum a posteriori (MAP) probability of the DRF. A procedure based on the iterated conditional modes algorithm and a metric-learning algorithm is developed to find a suboptimal MAP solution of the DRF. The proposed approach has been tested against various data sets. Experimental results demonstrate that our approach can enhance the clustering accuracy, and thus prove the feasibility of the proposed approach.
Author	Chang, Chin-Chun Chen, Hsin-Yi
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Cites_doi	10.1016/0031-3203(95)00120-4 10.1137/1.9781611972740.31 10.1007/s11263-006-7007-9 10.1109/ICDM.2008.65 10.1016/j.knosys.2009.06.009 10.1016/j.neucom.2009.03.012 10.1016/j.patcog.2007.10.004 10.1016/j.fss.2006.02.015 10.1007/s10618-009-0157-y 10.1016/S0167-8655(03)00177-6 10.1111/j.2517-6161.1977.tb01600.x 10.1016/j.patcog.2006.04.006 10.1109/CVPR.2008.4587451 10.1109/TPAMI.1979.4766903 10.1109/34.990138 10.1007/s10115-008-0134-6 10.1145/1281192.1281242 10.1007/s10618-005-0019-1 10.1016/j.patrec.2009.09.011 10.1111/j.2517-6161.1986.tb01412.x 10.1016/j.patcog.2005.12.012 10.1109/TNN.2005.845141 10.1016/j.neucom.2009.12.009 10.1145/1015330.1015360 10.1016/j.patcog.2010.02.022 10.1016/j.patcog.2007.04.010 10.1080/10556789908805766 10.1007/s10618-008-0103-4 10.1007/s10618-008-0104-3 10.1080/01969727308546046 10.1016/j.patcog.2009.11.005 10.1109/TPAMI.2004.1262179 10.1145/331499.331504 10.1007/s11222-007-9033-z 10.1007/s10994-008-5084-4 10.1007/978-3-540-24673-2_21 10.3233/IDA-2009-0399 10.1145/1014052.1014118 10.1145/1014052.1014062 10.1016/j.ijar.2007.03.008 10.1145/1823746.1823752 10.1109/TKDE.2007.190715 10.1137/1038003
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Keywords	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
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Snippet	Semi-supervised clustering exploits a small quantity of supervised information to improve the accuracy of data clustering. In this paper, a framework for...
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SubjectTerms	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
Title	Semi-supervised clustering with discriminative random fields
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