A matrix algorithm for mining association rules

Lecture Notes in Computer Science

Volumn 3644, Issue PART I, 2005, Pages 370-379

  Yuan, Yubo ^a   Huang, Tingzhu ^a  

^a UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

Algorithm; Association rules; Data mining; Matrix

Indexed keywords

ALGORITHMS; MATRIX ALGEBRA; PROBLEM SOLVING; RANDOM PROCESSES; SET THEORY; ASSOCIATION RULES; INTELLIGENT COMPUTING; LEARNING ALGORITHMS;

ASSOCIATION RULES; CANDIDATE SETS; MATRIX; APRIORI ALGORITHMS; DATA MINING PROBLEMS; MATRIX ALGORITHMS; MINING ASSOCIATIONS; NUMERICAL EXPERIMENTS; TEST PROBLEM;

DATA MINING;

EID: 27144444729     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/11538059_39     Document Type: Conference Paper

References (16)

1. Agrawal, R., Imielinski, T., Swami,A.: Database mining: A performance perspective. IEEE Trans. Knowledge and Data Eng., 5(6) (1993): 914-925.
2. Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the ACM SIGMOD Conference on Management of Data, 1993: 207-216.
3. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of International Conference on Very Large Data Bases, 1994: 487-499.
4. Park, J.S., Chen, M.S., Yu, P.S.: Using a hash-based method with transaction trimming for mining association rules, IEEE Trans. on Knowledge Data Engrg. 9(5) (1997): 813-825.
5. Pauray S.M. Tsai, Chien-Ming Chen: Discovering knowledge from large databases using prestored information. Information Systems. 26(1)(2001): 3-16.
6. Pauray S.M. Tsai, Chien-Ming Chen: Mining interesting association rules from customer databases and transaction databases. Information Systems. 29(3)(2004):685-696.
7. Han, J., Fu, Y.: Discovery of multiple-level association rules from large databases. Proceedings of the VLDB Conference. 1995: 420-431.
8. Srikant, R., Agrawal, R.: Mining generalized association rules. Proceedings of the VLDB Conference. 1995: 407-419.
9. Srikant, R., Agrawal, R.: Mining quantitative association rules in large relational tables. Proceedings of the ACM SIGMOD. 1996:1-12.
10. Lent, B., Swami, A., Widom, J.:Clustering association rules. Proceedings of the IEEE International Conference on Data Engineering. 1997:220-231.
11. Agrawal, R., Shafer, J.: Parallel mining of association rules. IEEE Trans. on Knowledge and Data Engg., 8(6) (1996): 962-969.
12. Hipp, J., Gontzer, U., Nakhaeizadeh, G.: Algorithms for association rule mining - a general survey and comparison. SIGKDD Explorations, 2(1) (2000): 58-64.
13. Berzal, F., Cubero, J.C., Marrin, N., Serrano, J.M.: TBAR: An efficient method for association rules mining in relational databases. Data and Knowledge Engineering 37(2001):47-64.
14. John D. Holt, Soon M. Chung: Mining association rules using inverted hashing and pruning,Information Processing Letters, 83(2002): 211-220.
15. Ping-Yu Hsu, en-Liang Chen, Chun-Ching Ling: Algorithms for mining association rules in bagdatabases. Information Science. 166(1)(2004):31-47.
16. Shichao Zhang, Jingli Lu, Chengqi Zhang: A fuzzy logic based method to acquireuser threshold of minimum-support for mining association rules. Information Science. 164(1)(2004):1-16.