A study on text clustering algorithms based on frequent term sets

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3584 LNAI, Issue , 2005, Pages 347-354

  Liu, Xiangwei ^a,b   He, Pilian ^a  

^a TIANJIN UNIVERSITY   (China)

^b TIANJIN POLYTECHNIC UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA MINING; DATABASE SYSTEMS; HIERARCHICAL SYSTEMS; PERFORMANCE; SET THEORY;

FREQUENT TERM SET-BASED HIERARCHICAL CLUSTERING ALGORITHM (FTSHC); FREQUENT TERM SETS; FTSC ALGORITHM; TEXT CLUSTERING ALGORITHMS;

TEXT PROCESSING;

EID: 26944455146     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11527503_42     Document Type: Conference Paper

References (9)

1. Lagus, K. and Kaski, S. "Keyword Selection Method for Characterizing Text/Document Maps". Proceeding of ICANN'99.
2. EI-Hamdouchi, A. and Willett, P. "An improved Algorithm for the Calculation of Exact Term Discrimination Values", Information Processing & Management, 24(1):17-22, 1988
3. Agrawal R, Imielimski T, and Swami A. "Mining association rules between sets of items in large databases". In Proc. 1993 ACM-SIGMOD Int. Conf. Management of Data, Washington DC. pp207-216, May 1993
4. Agrawal, R. and Srikant, R. "Fast algorithm for mining association rules in large databases", in Research Report RJ 9839,IBM Almaden Research Center, San Jose, CA, June.1994
5. Agrawal, R. and Srikant, R. "Fast algorithm for mining association rules". Proc. 1994 Int. Conf. Very Large Data Bases (VLDB'94), pp487-499, Santiago, Chile, Sep.1994.
6. http://www.daviddlewis.com/resources/testcollections
7. Yang,Y. and Pedersen, J.O. "A comparative study on feature selection in text categorization". Proc. of SIGIR'01, 2001
8. Bekkerman, R. and EI-Yaniv, R. "On feature distributional clustering for text categorization". Proc. Of SIGIR'01,2001
9. Macqueen, J. "Some methods for classification and analysis of multivariate observation". Proc.5th Berkeley Symp. Math. Statist, 1:281-297,1967