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A new hierarchical-clustering combination scheme based on scatter matrices and nearest neighbor criterion

机译:基于散点矩阵和最近邻准则的新的层次聚类组合方案

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In the field of pattern recognition, combination of different classifiers is a common method to improve classification accuracy. Recently, tendency to improve the function of clustering methods, specifically partitional clustering methods, is being increased. Generally, hierarchical clustering is preferred to partitional clustering when the number of exact clusters is undetermined or when we are interested in finding the relation between clusters. Most of the proposed methods for clustering combination are based on partitional clustering. In this paper, a new method for combination of hierarchical clustering is proposed. In this method, in the first step the primary dendrograms of base hierarchical clustering methods (such as Single Linkage and Complete Linkage) are converted to matrices. Then these matrices are synthesized together in a weighted procedure and led to a final description matrix. The weights are determined based on two criteria: clustering scatter matrices and nearest neighbour of each pattern. The results show improvement in function of combination method rather than base clustering methods.
机译:在模式识别领域,不同分类器的组合是提高分类精度的常用方法。近来,改善聚类方法,特别是分区聚类方法的功能的趋势正在增加。通常,当不确定的确切簇数或我们有兴趣寻找簇之间的关系时,优先使用分层簇而不是分区簇。提出的大多数聚类方法都是基于分区聚类的。本文提出了一种新的层次聚类相结合的方法。在此方法中,第一步是将基本层次聚类方法(例如“单链接”和“完整链接”)的主要树状图转换为矩阵。然后,这些矩阵在加权过程中一起合成,并生成最终描述矩阵。权重基于两个标准确定:聚类散布矩阵和每个模式的最近邻。结果表明组合方法比基本聚类方法的功能得到了改善。

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