Mining Generalized Term Associations: Count Propagation Algorithm

Proceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997

Volumn , Issue , 1997, Pages 203-206

  Kahng, Jonghyun ^a   Liao, Wen Hsiang Kevin ^a   McLeod, Dennis ^a  

^a University of Southern California   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE;

CO-OCCURRENCE; COLLECTION OF DOCUMENTS; COMPUTING TIME; DATA SET; GENERALIZED TERMS; MEDLINE; MEDLINE DATABASE; PROPAGATION ALGORITHM; RELEVANT TERMS; TERM ASSOCIATIONS;

TAXONOMIES;

EID: 26944450166     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (6)

1. Agrawal, R., and Srikant, R. 1994. Fast algorithms for mining association rules. In Proceedings of the 20th International Conference on Very Large Data Bases, pages 487-499, Santigo, Chile.
2. Arbib, M. et al. USC Brain Project. Refer to http://wwwhbp.usc.edu/.
3. Han, J., and Fu, Y. 1995. Discovery of multi-level association rules from large databases. In Proceedings of the 21st International Conference on Very Large Data Bases, pages 420-431, Zurich, Swizerland.
4. Park, J. S., Chen, M.-S., and Yu, P. S. 1995. An effective hash-based algorithm for mining association rules. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 175-186, San Jose, CA.
5. Savasere, A., Omiecinski, E., and Navathe, S. 1995. An efficient algorithm for mining association rules in large databases. In Proceedings of the 21st International Conference on Very Large Data Bases, pages 432-444, Zurich, Swizerland.
6. Srikant, R., and Agrawal, R. 1995. Mining generalized association rules. In Proceedings of the 21st International Conference on Very Large Data Bases, pages 407-419, Zurich, Swizerland.