A combined approach of formal concept analysis and text mining for concept based document clustering

被引：2

作者：

Myat, NN ^{[1
]}

Hla, KHS ^{[1
]}

机构：

[1] Univ Comp Studies, Yangon, Myanmar

来源：

2005 IEEE/WIC/ACM International Conference on Web Intelligence, Proceedings | 2005年

关键词：

conceptual document clustering; formal concept analysis (FCA); frequent termsets; association; correlation;

D O I：

10.1109/WI.2005.1

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Nowadays, the demand of conceptual document clustering is becoming increase to manage various types of vast amount of information published on the World Wide Web. In this paper, we use Formal Concept Analysis (FCA) method for clustering documents according to their formal contexts. Concept hierarchy of documents is built using the formal concepts of the documents in the document corpus. We use tf.idf (term frequency x inverse document frequency) term weighting model to reduce less useful concepts from these formal concepts and the association and correlation mining techniques to analyze the relationship of terms in the document corpus.

引用

页码：330 / 333

页数：4

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