Abstract
Recently, the weakness of the canonical support-confidence framework for associations mining has been widely studied in the literature. One of the difficulties in applying association rules mining to real world applications is the setting of support constraint. A high support constraint avoids the combinatorial explosion in discovering frequent itemsets, but at the expense of missing interesting patterns of low support. Instead of seeking the way for setting the appropriate support constraint, all current approaches leave the users in charge of the support setting, which, however, puts the users in a dilemma. This paper is an effort to answer this long-standing open question. Based on the notion of confidence and lift measures, we propose an automatic support specification for mining high confidence and positive lift associations without consulting the users. Experimental results show that this specification is good at discovering the low support, but high confidence and positive lift associations, and is effective in reducing the spurious frequent itemsets.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Agrawal, R., Imielinski T., Swami, A.: Mining association rules between sets of items in large databases. Proc. 1993 ACM-SIGMOD Int. Conf. Management of Data. Washington, D.C. (1993) 207–216
Aggarwal, C. C., Yu, P. S.: A new framework for itemset generation. Proc. 17th ACM Symp. Principles of Database Systems. Seattle, WA (1998) 18–24
Berry, J. A., Linoff, G. S.: Data Mining Techniques for Marketing, Sales and Customer Support. John Wiley & Sons, Inc. (1997)
Brin, S., Motwani, R., Silverstein, C.: Beyond market baskets: generalizing association rules to correlations. Proc. 1997 ACM-SIGMOD Int. Conf. Management of Data. (1997) 265–276
Brin, S., Motwani, R., Ullman, J.D., Tsur, S.: Dynamic itemset counting and implication rules for market-basket data. Proc. 1997 ACM-SIGMOD Int. Conf. Management of Data. (1997) 207–216
Cohen, E., Datar, M., Fujiwara, S., Gionis, A., Indyk, P., Motwani, R., Ullman, J. D., Yang, C.: Finding Interesting associations without support pruning. Proc. IEEE Int. Conf. Data Engineering. (2000) 489–499
Han, J., Fu, Y.: Discovery of multiple-level association rules from large databases. Proc. 21st Int. Conf. Very Large Data Bases. Zurich, Swizerland (1995) 420–431
Li, J., Zhang, X.: Efficient mining of high confidence association rules without support thresholds. Proc. 3rd European Conf. Principles and Practice of Knowledge Discovery in Databases. Prague (1999)
Liu, B., Hsu, W., Ma, Y.: Mining association rules with multiple minimum supports. Proc. 1999 ACM-SIGKDD Int. Conf. Knowledge Discovery and Data Mining. San Deigo, CA (1999) 337–341
Seno, M., Karypis, G.: LPMiner: An algorithm for finding frequent intemsets using length-decreasing Support constraint. Proc. 1st IEEE Int. Conf. Data Mining. (2001)
Tseng, M.-C., Lin, W.-Y.: Mining generalized association rules with multiple minimum supports. Proc. Int. Conf. Data Warehousing and Knowledge Discovery. Munich, Germany (2001) 11–20
Wang, K., He, Y., Han, J.: Mining frequent itemsets using support constraints. Proc. 26th Int. Conf. Very Large Data Bases. Cario, Egypt (2000) 43–52
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lin, WY., Tseng, MC., Su, JH. (2002). A Confidence-Lift Support Specification for Interesting Associations Mining. In: Chen, MS., Yu, P.S., Liu, B. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2002. Lecture Notes in Computer Science(), vol 2336. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47887-6_14
Download citation
DOI: https://doi.org/10.1007/3-540-47887-6_14
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43704-8
Online ISBN: 978-3-540-47887-4
eBook Packages: Springer Book Archive