二部图划分中的模式保持

Tianming Hu, Chao Qu, C. Tan, S. Sung, Wenjun Zhou
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引用次数: 5

摘要

本文描述了一种新的词-文档共聚类的二部分公式,使得在这种情况下的高团模式,即强关联文档,保证不会分裂成不同的聚类。我们的模式保持聚类方法包括三个步骤:挖掘最大的超团模式,形成二部,划分它。由于保留了文档的超团模式,每个聚类的主题可以由该聚类的最上面的单词和模式中的文档来表示,这比标准的二分公式中的文档更紧凑和更具代表性。实际数据集的实验表明,以hyperclique模式为起点,我们可以在各种外部聚类标准方面改善聚类结果。此外,带有保留主题文档集的分区二部自然适合搜索引擎中的不同功能
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Preserving Patterns in Bipartite Graph Partitioning
This paper describes a new bipartite formulation for word-document co-clustering such that hyperclique patterns, strongly affiliated documents in this case, are guaranteed not to be split into different clusters. Our approach for pattern preserving clustering consists of three steps: mine maximal hyperclique patterns, form the bipartite, and partition it. With hyperclique patterns of documents preserved, the topic of each cluster can be represented by both the top words from that cluster and the documents in the patterns, which are expected to be more compact and representative than those in the standard bipartite formulation. Experiments with real-world datasets show that, with hyperclique patterns as starting points, we can improve the clustering results in terms of various external clustering criteria. Also, the partitioned bipartite with preserved topical sets of documents naturally lends itself to different functions in search engines
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