Title :
A combined approach of formal concept analysis and text mining for concept based document clustering
Author :
Myat, Nyeint Nyeint ; Hla, Khin Haymar Saw
Abstract :
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 × 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.
Keywords :
Internet; data mining; text analysis; World Wide Web; concept based document clustering; correlation mining; formal concept analysis; inverse document frequency; term frequency; text mining; Clustering algorithms; Frequency; Information retrieval; Itemsets; Organizing; Partitioning algorithms; Publishing; Search engines; Text mining; Web sites; association; conceptual document clustering; correlation; formal concept analysis (FCA); frequent termsets;
Conference_Titel :
Web Intelligence, 2005. Proceedings. The 2005 IEEE/WIC/ACM International Conference on
Print_ISBN :
0-7695-2415-X
