Title :
Dynamic Fluzzy Clustering Algorithm for Web Documents Mining
Author_Institution :
Dept. of Comput. Sci., Weinan Teachers Coll., Weinan, China
Abstract :
This paper first studies the methods of web documents mining and text clustering, and summaries the fuzzy clustering algorithms and similarity measure functions, then proposes a modified similarity function which can solve the problems of feature selection and feature extraction in high-dimensional space. Finally, this paper puts forward to a dynamic fluzzy clustering algorithm(DCFCM) by combining the proposed similarity function with approximated C-mediods. The experiments show that DCFCM can effectively improve he precision of web documents clustering, the method is feasible in web documents mining.
Keywords :
Internet; data mining; feature extraction; pattern clustering; text analysis; Web documents mining; approximated C-mediods; dynamic fluzzy clustering algorithm; feature extraction; feature selection; similarity measure functions; text clustering; document clustering; fuzzy clustering; similarity measure function; text mining;
Conference_Titel :
Computational Intelligence and Security (CIS), 2010 International Conference on
Conference_Location :
Nanning
Print_ISBN :
978-1-4244-9114-8
Electronic_ISBN :
978-0-7695-4297-3
DOI :
10.1109/CIS.2010.21