Abstract
Most existing algorithms employ a uniform minimum support for mining association rules. Nevertheless, each item in a publication database, even each set of items, is exhibited in an individual period. A reasonable minimum support threshold has to be adjusted according to the exhibition period of each k-itemset. Accordingly, this paper proposes a new algorithm, called WMS, for mining association rules with weighted minimum supports in publication databases. WMS discovers all frequent itemsets which satisfy their individual requirement of minimum support thresholds. WMS applies the group closure property to prune futile itemsets, to reduce the number of candidates generated, and thus to generate the candidate sets efficiently.
Chapter PDF
References
R. C. Agarwal, et al. A tree projection algorithm for generation of frequent itemsets. Journal of Parallel and Distributed Computing, 2000, 61(3): 350–361
C. C. Aggarwal and P. S. Yu, Mining associations with the collective strength approach. IEEE Trans. Knowledge and Data Engineering, 2001, 13(6): 863–873
R. Agrawal, et al. Mining association rules between sets of items in large databases. 1993 ACM SIGMOD Intl. Conf. on Management of Data, Washington: 207–216
R. Agrawal and R. Srikant, Fast algorithms for mining association rules. 20th Intl. Conf on Very Large Data Bases, 1994, Santiago: 487–499.
C. H. Cai, et al. Mining association rules with weighted items. 1998 Intl. Database Engineering and Applications Symposium, Cardiff: 68–77
M. S. Chen, et al. Data mining: An overview from a database perspective. IEEE Trans. Knowledge Data Engineering, 1996, 8(6): 866–883
F. L. Chung and C. L. Lui, A post-analysis framework for mining generalized association rules with multiple minimum supports. 6th ACM SIGKDD Intl. Conf on Knowledge Discovery and Data Mining Workshop on Post-Processing in Machine Learning and Data Mining, 2000, Boston: 9–14
J. Han, et al. Mining frequent patterns without candidate generation. 2000 ACM-SIGMOD Intl. Conf. on Management of Data, Dallas: 1–12
C. H. Lee, et al. Progressive partition miner: An efficient algorithm for mining general temporal association rules. IEEE Trans. Knowledge Data Engineering, 2003, 15(4):1004–1017
C. L. Liu and F. L. Chung, Discovery of generalized association rules with multiple minimum supports. 4th European Conf. on Principles of Data Mining and Knowledge Discovery, 2000, Lyon: 510–515
B. Liu, et al. Mining association rules with multiple minimum support. 1999 ACM SIGKDD Intl. Conf on Knowledge Discovery and Data Mining, San Diego: 337–341
J. S. Park et al. An effective hash-based algorithm for mining association rules. 1995 ACM-SIGMOD Intl. Conf. on Management of Data, San Jose: 175–186
M. Seno and Karypis, LPMiner: An algorithm for finding frequent itemsets using length-decreasing support constraint. 2001 IEEE Intl. Conf on Data Mining, San Jose: 505–512
M. C. Tseng, et al. Maintenance of generalized association rules with multiple minimum support. Joint 9th IFSA World Congress and 20th NAFIPS Intl. Conf, 2001, Vancouver: 1294–1299
K. Wang, et al. Pushing support constraints into association rules mining. IEEE Trans. Knowledge Data Engineering, 2003, 15(3): 642–658
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 International Federation for Information Processing
About this paper
Cite this paper
Li, Y., Chang, C., Yeh, J. (2005). An Algorithm for Mining Association Rules with Weighted Minimum Supports. In: Li, D., Wang, B. (eds) Artificial Intelligence Applications and Innovations. AIAI 2005. IFIP — The International Federation for Information Processing, vol 187. Springer, Boston, MA. https://doi.org/10.1007/0-387-29295-0_31
Download citation
DOI: https://doi.org/10.1007/0-387-29295-0_31
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-28318-0
Online ISBN: 978-0-387-29295-3
eBook Packages: Computer ScienceComputer Science (R0)