A Tree Projection Algorithm for Generation of Frequent Item Sets

Journal of Parallel and Distributed Computing

Volumn 61, Issue 3, 2001, Pages 350-371

  Agarwal, Ramesh C ^a   Aggarwal, Charu C ^a   Prasad, V V V ^a  

^a IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

Association rules; data mining; caching; item sets

Indexed keywords

EID: 0141764033     PISSN: 07437315     EISSN: None     Source Type: Journal    
DOI: 10.1006/jpdc.2000.1693     Document Type: Article

References (17)

1. R. C. Agarwal, C. C. Aggarwal, V. V. V. Prasad, and, V. Crestana, A Tree Projection Algorithm for Generation of Large Itemsets For Association Rules, IBM Research Report, RC 21341.
2. Agarwal R. C., Aggarwal C. C., Prasad V. V. V. Depth first generation of long patterns. Proceedings of the ACM SIGKDD Conference. 2000.
3. Agrawal R., Imielinski T., Swami A. Mining association rules between sets of items in very large databases. Proceedings of the ACM SIGMOD Conference on Management of Data. 1993;. p. 207-216.
4. R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A. I. Verkamo, Fast discovery of association rules, in Advances in Knowledge Discovery and Data Mining, Chap. 12, pp. 307-328, AAAI/MIT Press, Cambridge, MA.
5. Agrawal R., Shafer J. C. Parallel mining of association rules. IEEE Trans. Knowledge Data Engineering. 8:1996;962-969.
6. Bayardo R. J. Efficiently mining long patterns from databases. Proceedings of the ACM SIGMOD. 1998;. p. 85-93.
7. Brin S., Motwani R., Ullman J. D., Tsur S. Dynamic itemset counting and implication rules for market basket data. Proceedings of the ACM SIGMOD. 1997;. p. 255-264.
8. Han E.-H., Karypis G., Kumar V. Scalable parallel data mining for association rules. Proceedings of the ACM SIGMOD Conference. 1997;. p. 277-288.
9. Han J., Fu Y. Discovery of multi-level association rules from large databases. Proceedings of the International Conference on Very Large Databases, Zurich, Switzerland. September 1995;. p. 420-431.
10. Kumar V., Grama A., Gupta A., Karypis G. Introduction to Parallel Computing: Algorithm Design and Analysis. 1994;Benjamin Cummings-Addison Wesley, Redword City.
11. Lin D., Kedem Z. M. Pincer-search: A new algorithm for discovering the maximum frequent itemset. EDBT Conference Proceedings. 1998;. p. 105-119.
12. Mannila H., Toivonen H., Verkamo A. I. Efficient algorithms for discovering association rules. AAAI Workshop on Knowledge Discovery in Databases. 1994;. p. 181-192.
13. Savasere A., Omiecinski E., Navathe S. B. An efficient algorithm for mining association rules in large databases. Proceedings of the 21st International Conference on Very Large Databases. 1995.
14. Srikant R., Agrawal R. Mining Generalized Association Rules. Proceedings of the 21st International Conference on Very Large Data Bases. 1995;. p. 407-419.
15. Srikant R., Agrawal R. Mining quantitative association rules in large relational tables. Proceedings of the ACM SIGMOD Conference on Management of Data. 1996;. p. 1-12.
16. Toivonen H. Sampling large databases for association rules. Proceedings of the 22nd International Conference on Very Large Databases, Bombay, India. September 1996.
17. Zaki M. J., Parthasarathy S., Ogihara M., Li W. New algorithms for fast discovery of association rules. KDD Conference Proceedings. 1997;. p. 283-286.