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| 摘要: |
| 已有的聚类集算法基本上都是非监督聚类集成算法，这样不能利用已知信息，使得聚类集成的准确性、鲁棒性和稳定性降低。把半监督学习和聚类集成结合起来，设计半监督聚类集成模型来克服这些缺点。主要工作包括:第一，设计了基于贝叶斯网络的半监督聚类集成(semi-supervised cluster ensemble，简称SCE)模型，并对模型用变分法进行了推理求解;第二，在此基础上，给出了EM(expectation maximization)框架下的具体算法;第三，从UCI(University of Califor |
| 关键词: 半监督聚类集成 变分推理 必连 不连 |
| DOI： |
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| 基金项目:Supported by the National Natural Science Foundation of China under Grant No.61003142 (国家自然科学基金); the Program of the Ministry of Railways of China under Grant Nos.2009X010-A, 2009X010-B (国家铁道部资助项目) |
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| Semi-Supervised Cluster Ensemble Model Based on Bayesian Network |
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WANG Hong-Jun, LI Zhi-Shu, QI Jian-Huai, CHENG Yang, ZHOU Peng, ZHOU Wei
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| Abstract: |
| The existing algorithms are mostly unsupervised algorithms of a cluster ensemble, which cannot take advantages of known information of datasets. As a result, the precision, robustness, and stability of a cluster ensemble are degraded. To conquer these disadvantages, a semi-supervised cluster ensemble (SCE) model, based on both semi-supervised learning and ensemble learning technologies, is designed in this paper. There are three main works in this paper. The first is that SCE is proposed, and the variational inference oriented SCE is illustrated in detail. The second is based on the above work: an EM (expectation maximization) algorithm of SCE is illustrated step by step. The third is that some datasets are drawn from the UCI (University of California, Irvine) machine learning database for experiments which show that both SCE and its EM algorithm are good for semi-supervised cluster ensemble and outperforms NMFS (algorithm of nonnegative-matrix-factorization based semi-supervised), semi-supervised SVM (support vector machine), and LVCE (latent variable model for cluster ensemble). The Semi-Supervised Cluster Ensemble model is first stated in this paper, and this paper includes the advantages of both the semi-supervise learning and the cluster ensemble. Therefore, its result is better than the results of semi-learning clustering and cluster ensemble. |
| Key words: semi-supervised cluster ensemble variational inference must link can not link |
