Semi-supervised Artificial Immune Mixture Models Clustering

HUANG Wenlong; WANG Xiaodan; LI Guohong; KANG Hongxia

doi:10.1049/cje.2016.03.009

Volume 25 Issue 2

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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

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Semi-supervised Artificial Immune Mixture Models Clustering

doi: 10.1049/cje.2016.03.009

1.
Air and Missile Defense College, Air Force Engineering University, Xi'an 710051, China;
2.
State Key Laboratory of Astronautic Dynamics, Xi'an Satellite Control Center, Xi'an 710043, China

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

Abstract

Abstract

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.
- Semi-supervised clustering,
- Mixture model,
- Immune clonal selection,
- Globally optimal solution

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References(19)

References

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