K-means based method for overlapping document clustering

被引：2

作者：

Beltran, Beatriz ^{[1
]}

Vilarino, Darnes ^{[1
]}

Martinez-Trinidad, Jose Fco. ^{[2
]}

Carrasco-Ochoa, J. A. ^{[2
]}

Pinto, David ^{[1
]}

机构：

[1] Benemerita Univ Autonoma Puebla, Language & Knowledge Engn Lab, Puebla, Mexico

[2] Inst Nacl Astrofis Opt & Electr, Comp Sci, Puebla, Mexico

来源：

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS | 2020年 / 39卷 / 02期

关键词：

Clustering; overlapping clustering; document clustering; ALGORITHM; DENSITY;

D O I：

10.3233/JIFS-179878

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Overlapping clustering algorithms have shown to be effective for clustering documents. However, the current overlapping document clustering algorithms produce a big number of clusters, which make them little useful for the user. Therefore, in this paper, we propose a k-means based method for overlapping document clustering, which allows to specify by the user the number of groups to be built. Our experiments with different corpora show that our proposal allows obtaining better results in terms of FBcubed than other recent works for overlapping document clustering reported in the literature.

引用

页码：2127 / 2135

页数：9

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