DDOC: Overlapping Clustering of Words for Document Classification

机译：DDOC：用于文档分类的单词的重叠聚类

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In this paper we study the interest of integration of an overlapping clustering approach rather than traditional hard-clustering ones, in the context of dimensionality reduction of the description space for document classification. The Distributional Divisive Overlapping Clustering (DDOC) method is briefly presented and compared to Agglomerative Distributional Clustering (ADC) and Information-Theoretical Divisive Clustering (ITDC) on the two corpus Reuters-21578 and 20Newsgroup.

机译：在本文中，我们在减少文档分类描述空间的维数的背景下，研究了集成重叠聚类方法而不是传统的硬聚类方法的兴趣。简要介绍了分布分裂重叠聚类（DDOC）方法，并将其与两个语料库Reuters-21578和20Newsgroup上的聚集分布聚类（ADC）和信息理论分裂聚类（ITDC）进行了比较。

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来源
《International Conference on String Processing and Information Retrieval(SPIRE 2004); 20041005-08; Padova(IT)》|2004年|P.127-128|共2页
会议地点 Padova(IT)
作者
Guillaume Cleuziou; Lionel Martin; Viviane Clavier; Christel Vrain;
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LIFO, Laboratoire d'Informatique Fondamentale d'Orleans Rue Leonard de Vinci B.P. 6759, 45067 Orleans cedex2, France;

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正文语种 eng
中图分类数据备份与恢复;
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7. Figure 12: Comparison of the Silhouette Coefficient (A), Dunn Index (B), number of clusters (C) obtained by different clustering algorithms and overlapping between the clusters’ relevant words (D). [O] . -1

机译：图12：轮廓系数（A），DUNN索引（B），不同聚类算法获得的簇数（C）的比较和簇之间的相关单词（D）之间的重叠。

DDOC: Overlapping Clustering of Words for Document Classification

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