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Incremental Mixture Learning for Clustering Discrete Data

机译:增量混合学习用于聚类离散数据

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This paper elaborates on an efficient approach for clustering discrete data by incrementally building multinomial mixture models through likelihood maximization using the Expectation-Maximization (EM) algorithm. The method adds sequentially at each step a new multinomial component to a mixture model based on a combined scheme of global and local search in order to deal with the initialization problem of the EM algorithm. In the global search phase several initial values are examined for the parameters of the multinomial component. These values are selected from an appropriately defined set of initialization candidates. Two methods are proposed here to specify the elements of this set based on the agglomerative and the kd-tree clustering algorithms. We investigate the performance of the incremental learning technique on a synthetic and a real dataset and also provide comparative results with the standard EM-based multinomial mixture model.
机译:本文通过使用期望最大化(EM)算法,通过递增通过似然最大化来逐渐构建多项式混合模型来精确聚类离散数据的有效方法。该方法在每个步骤中顺序地基于全局和本地搜索的组合方案对混合模型顺序地添加到混合模型中,以便处理EM算法的初始化问题。在全局搜索阶段,检查多项组件的参数的几个初始值。这些值选自适当定义的一组初始化候选候选。这里提出了两种方法来基于附聚物和KD树聚类算法指定该组的元素。我们调查了增量学习技术对合成和真实数据集的性能,并提供了与标准EM的多项式混合模型的比较结果。

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