HUANG Wenlong, WANG Xiaodan, LI Guohong, et al., “Semi-supervised Artificial Immune Mixture Models Clustering,” Chinese Journal of Electronics, vol. 25, no. 2, pp. 249-255, 2016, doi: 10.1049/cje.2016.03.009
Citation: HUANG Wenlong, WANG Xiaodan, LI Guohong, et al., “Semi-supervised Artificial Immune Mixture Models Clustering,” Chinese Journal of Electronics, vol. 25, no. 2, pp. 249-255, 2016, doi: 10.1049/cje.2016.03.009

Semi-supervised Artificial Immune Mixture Models Clustering

doi: 10.1049/cje.2016.03.009
Funds:  This work is supported by the National Natural Science Foundation of China (No.61273275, No.61573375), the Aviation Science Foundation of China (No.20151996015), the Open Research Fund of State Key Laboratory of Astronautic Dynamics (No.2012ADL-DW0202), and General Financial Grant from China Postdoctoral Science Foundation (No.2015M572778).
  • Received Date: 2014-05-15
  • Rev Recd Date: 2014-09-30
  • Publish Date: 2016-03-10
  • Learning with partly labeled data aims at combining labeled and unlabeled data in order to boost the accuracy of an algorithm. The traditional Expectation maximization (EM) algorithm only produces locally optimal solutions, it is sensitive to initialization, and the number of components of mixture model must be known in advance. We propose a novel semi-supervised clustering algorithm that uses Gaussian mixture models (GMM) as the underlying clustering model. A novel adaptive global search mechanism is introduced into semi-supervised gaussian mixture model-based clustering, where the EM algorithm is incorporated with the ideas of an immune clonal selection technique. The new algorithm overcomes the various problems associated with the traditional EM algorithm. And it can improve the effectiveness in estimating the parameters and determining the optimal number of clusters automatically. The experimental results illustrate the proposed algorithm provides significantly better clustering results, when compared with other methods of incorporating equivalence constraints.
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  • K. Wagstaff, C. Cardie, S. Rogers, et al., "Constrained k-means clustering with background knowledge", Proc. of the Eighteenth International Conference on Machine Learning, New York, USA, pp.577-584, 2001.
    B. Kulis, S. Basu, I. Dhillon, et al., "Semi-supervised graph clustering: A kernel approach", Proc. of the 22nd International Conference on Machine learning, New York, USA, pp.457-464, 2005.
    D. Klein, S.D. Kamvar and C.D. Manning, "From instancelevel constraints to space-level constraints: Making the most of prior knowledge in data clustering", Proc. of 19th International Conference on Machine Learning, Morgan Kaufmann, San Francisco, USA, pp.307-314, 2002.
    N. Wang and X. Li, "Active semi-supervised spectral clustering based on pairwise constraints", Acta Electronica Sinica, Vol.38, No.1, pp.172-176, 2010. (in Chinese)
    A. Abdullin and O. Nasraoui, "Clustering heterogeneous data with mutual semi-supervision", Proc. of the 19th International Conference on String Processing and Information Retrieval, Cartagena de Indias, Colombia, pp.18-29, 2012.
    W.F. Chen and G.C. Feng, "Spectral clustering: A semisupervised approach", Neurocomputing, Vol.77, No.1, pp.229-242, 2012.
    C.F. Gao and X.J. Wu, "A new semi-supervised clustering algorithm with pairwise constraints by competitive agglomeration", Applied Soft Computing, Vol.11, No.8, pp.5281-5291, 2011.
    E.B. Ahmed, A.N. and F. Gargouri, "A new semi-supervised hierarchical active clustering based on ranking constraints for analysts groupization", Applied Intelligence, Vol.39, No.2, pp.236-250, 2013.
    N. Shental, A. Bar-Hillel, T. Hertz, et al., "Computing gaussian mixture models with EM using equivalence constraints", Proc. of the Advances in Neural Information Processing Systems, Vancouver, Canada, pp.465-472, 2004.
    T. Lange, M. Law, A. Jain, et al., "Learning with constrained and unlabelled data", Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, DC, USA, Vol.3, pp.731-738, 2005.
    Z.D. Lu and T.K. Leen, "Semi-supervised learning with penalized probabilistic clustering", Proc. of the Advances in Neural Information Processing Systems, Vancouver, Canada, pp.849-856, 2005.
    Q. Zhao and D.J. Miller, "Mixture modeling with pairwise, instance-level class constraints", Neural Computation, Vol.17, No.11, pp.2482-2507, 2005.
    E. Hart, "Immunology as a metaphor for computational information processing: Fact or fiction", Ph.D. Thesis, University of Edinburgh at Scotland, UK, 2002.
    H.U. Berna and K.K. Sadan, "A review of clonal selection algorithm and its applications", Artificial Intelligence Review, Vol.36, No.2, pp.117-138, 2011.
    H.F. Du and L.C. Jiao, "Artificial immune system: Progress and prospect", Acta Electronica Sinica, Vol.31, No.10, pp.1540-1548, 2003. (in Chinese)
    D.J. Newman, S. Hettich, C.L. Blake, et al., "UCI repository of machine learning databases",, Irvine, CA: University of California, Department of Information and Computer Science, 1998.
    M. Halkidi, D. Gunopulos, M. Vazirgiannis, et al., "A clustering framework based on subjective and objective validity criteria", ACM Transactions on Knowledge Discovery from Data, Vol.1, No.4, pp.1-25, 2008.
    M. Meil, "Comparing clusterings by the variation of information", Proc.16th Annual Conference on Computational Learning Theory, Washington, DC, USA, pp.173-187, 2012.
    X. Geng, D.C. Zhan and Z.H. Zhou, "Supervised nonlinear dimensionality reduction for visualization and classification", IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics, Vol.35, No.6, pp.1098-1107, 2005.
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