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Partitional vs Hierarchical Clustering Using a Minimum Grammar Complexity Approach

机译:使用最小语法复杂度方法的分区聚类与分层聚类

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摘要

This paper addresses the problem of structural clustering of string patterns. Adopting the grammar formalism for representing both individual sequences and sets of patterns, a partitional clustering algorithm is proposed. The performance of the new algorithm, taking as reference the corresponding hierarchical version, is analyzed in terms of computational complexity and data partitioning results. The new algorithm introduces great improvements in terms of computational efficiency, as demonstrated by theoretical analysis. Unlike the hierarchical approach, clustering results are dependent on the order of patterns' presentation, which may lead to performance degradation. This effect, however, is overcome by adopting a resampling technique. Empirical evaluation of the methods is performed through application examples, by matching clusters between pairs of partitions and determining an index of clusters agreement.
机译:本文解决了字符串模式的结构聚类问题。通过采用语法形式主义来表示单个序列和模式集,提出了一种分区聚类算法。从计算复杂度和数据划分结果的角度分析了新算法的性能,并以相应的分层版本作为参考。如理论分析所示,新算法在计算效率方面引入了极大的改进。与分层方法不同,聚类结果取决于模式表示的顺序,这可能导致性能下降。但是,通过采用重采样技术可以克服此影响。通过应用示例,通过匹配分区对之间的集群并确定集群协议的索引,可以对方法进行经验评估。

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