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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| Published in: | Pattern recognition Vol. 45; no. 12; pp. 4402 - 4413 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.12.2012
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| 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. |
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| 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 |
| Author_xml | – sequence: 1 givenname: Chin-Chun surname: Chang fullname: Chang, Chin-Chun email: cvml@mail.ntou.edu.tw – sequence: 2 givenname: Hsin-Yi surname: Chen fullname: 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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