Association rules mining using heavy itemsets

Data and Knowledge Engineering

Volumn 61, Issue 1, 2007, Pages 93-113

  Palshikar, Girish K ^a   Kale, Mandar S ^b   Apte, Manoj M ^b  

^a TATA RESEARCH DEVELOPMENT AND DESIGN CENTRE   (India)

^b TATA CONSULTANCY SERVICES   (India)

Author keywords

Association rules; Data mining; Knowledge compression; Knowledge discovery in databases

Indexed keywords

ALGORITHMS; DATA MINING; DATABASE SYSTEMS; PROBLEM SOLVING; USER INTERFACES;

ASSOCIATION RULES; KNOWLEDGE COMPRESSION; KNOWLEDGE DISCOVERY;

LOGIC PROGRAMMING;

EID: 33847113992     PISSN: 0169023X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.datak.2006.04.009     Document Type: Article

References (17)

1. R. Agrawal, T. Imielinski, A. Swami, Mining associations between sets of items in massive databases, in: Proc. ACM SIGMOD Int. Conf. Management of Data (SIGMOD 1993), pp. 207-216.
2. S. Brin, R. Motwani, J. Ullman, S. Tsur, Dynamic itemset counting and implication rules for market basket data, in: Proc. ACM SIGMOD Int. Conf. Management of Data (SIGMOD 1997), 1997, pp. 255-264.
3. Cohen E., Datar M., Fujiwara S., Gionis A., Indyk P., Motwani R., Ullman J.D., and Yang C. Finding interesting associations without support pruning. Knowledge Data Eng. 13 1 (2001) 64-78
4. J. Han, J. Pei, Y. Yin, Mining frequent patterns without candidate generation, in: Proc. ACM SIGMOD Int. Conf. Management of Data (SIGMOD 2000), 2000, pp. 1-12.
5. R.J. Hilderman, H.J. Hamilton, Knowledge discovery and interestingness measures: a survey, Tech. Report CS-99-04, Dept. of Computer Science, Univ. of Regina, 1999.
6. Karp R.M., Shenker S., and Papadimitrious C.H. A simple algorithm for finding frequent elements in streams and bags. ACM Trans. Database Syst. 28 1 (2003) 51-55
7. Lin D., and Kedem Z.M. Pincer-search: an efficient algorithm for discovering the maximum frequent set. IEEE Trans. Knowledge Data Eng. 14 3 (2002) 553-556
8. B. Liu, W. Hsu, Y. Ma, Pruning and summarizing the discovered associations, in: Proc. 5th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, New York, 1999, pp. 125-134.
9. G.K. Palshikar, M.S. Kale, M.M. Apte, Association rule mining using heavy itemsets, in: Proc. Int. Conf. Management of Data (COMAD 2005), Goa, India, 6-8 January 2005, pp. 148-155.
10. Pujari A.K. Data Mining Techniques (2001), University Press
11. S. Ramaswamy, S. Mahajan, A. Silberschatz, On the discovery of interesting patterns in association rules, in: Proc. 24th Int. Conf. Very Large Data Bases (VLDB 1998), 1998, pp. 368-379.
12. P. Shenoy, J.R. Haritsa, S. Sudarshan, G. Bhalotia, M. Bawa, D. Shah, Turbo-charging vertical mining of large databases, in: Proc. ACM SIGMOD Int. Conf. Management of Data (SIGMOD 2000), pp. 22-33.
13. Soo P.J., Ming-Syan C., and Yu P.S. Using a Hash-based method with transaction trimming for mining association rules. IEEE Trans. Knowledge Data Eng. 9 5 (1997) 813-825
14. H. Tiovonen, Sampling large databases for association rules, in: Proc. 22nd Int. Conf. Very Large Databases (VLDB 1996), 1996, pp. 134-145.
15. M.J. Zaki, Generating non-redundant association rules, in: Proc. 6th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining (KDD-2000), 2000, pp. 34-43.
16. M.J. Zaki, C. Hsiao, Charm: an efficient algorithm for closed itemset mining, in: Proc. SIAM International Conference on Data Mining, 2002.
17. Z. Zheng, R. Kohavi, L. Mason, Real world performance of association rule algorithms, in: Proc. 7th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining (KDD-2001), 2001, pp. 401-406.