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首页> 外文期刊>Chinese Journal of Electronics >Semi-supervised Artificial Immune Mixture Models Clustering
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Semi-supervised Artificial Immune Mixture Models Clustering

机译：半监督人工免疫混合物模型聚类

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摘要

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.
机译：使用部分标记的数据进行学习旨在将标记的数据和未标记的数据进行组合，以提高算法的准确性。传统的期望最大化（EM）算法仅产生局部最优解，它对初始化敏感，并且必须事先知道混合模型的组件数。我们提出了一种新颖的半监督聚类算法，该算法使用高斯混合模型（GMM）作为基础聚类模型。一种新型的自适应全局搜索机制被引入到基于半监督高斯混合模型的聚类中，其中EM算法与免疫克隆选择技术的思想相结合。新算法克服了与传统EM算法相关的各种问题。并且可以提高参数估计和自动确定最佳簇数的有效性。实验结果表明，与采用等价约束的其他方法相比，该算法可提供更好的聚类结果。

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