Fuzzy clustering and categorization of text documents

被引：0
|
作者：
Ayeldeen, Heba [1 ]
Rassanien, Aboul Ella [1 ]
Fahmy, Aly Aly [1 ]
机构：
[1] Cairo Univ, Fac Comp & Informat, Cairo, Egypt
来源：
2013 13TH INTERNATIONAL CONFERENCE ON HYBRID INTELLIGENT SYSTEMS (HIS) | 2013年
关键词：
Lexical similarity; Medical Ontology; MeSH; Fuzzy Euclidean distance Algorithm;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The fuzzy Euclidean distance clustering algorithm has been well studied and used in information retrieval society for clustering documents. However. the fuzzy logic algorithm poses problems in dealing with large amount of data. in this paper we proposed results for clustering theses documents based on Euclidean distances and cluster-dependent keyword weighting. The proposed approach is based on the Fuzzy Euclidean distance clustering algorithm. The cluster dependent keyword weighting help in partitioning and categorizing the theses documents into more meaningful categories.
引用
收藏
页码：262 / 266
页数：5
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