2004 EfficientMineofBothPosandNegAssocRules

• (Wu et al., 2004) ⇒ Xindong Wu, Chengqi Zhang, and Shichao Zhang. (2004). “Efficient Mining of Both Positive and Negative Association Rules.” In: Journal ACM Transactions on Information Systems (TOIS) (22:3). doi:10.1145/1010614.1010616.

Subject Headings: Association rules, Negative associations.

Cited By

• ~154 http://scholar.google.com/scholar?q=%22Efficient+Mining+of+Both+Positive+and+Negative+Association+Rules%22+2004

Quotes

Abstract

This paper presents an efficient method for mining both positive and negative association rules in databases. The method extends traditional associations to include association rules of forms A ⇒ ¬ B, ¬ A ⇒ B, and ¬ A ⇒ ¬ B, which indicate negative associations between itemsets. With a pruning strategy and an interestingness measure, our method scales to large databases. The method has been evaluated using both synthetic and real-world databases, and our experimental results demonstrate its effectiveness and efficiency.

References

• AGGARAWAL, C. AND YU, P. 1998. A new framework for itemset generation. In: Proceedings of the Seventeenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. ACM, Seattle, Washington, 18–24.
• AGRAWAL, R., IMIELINSKI, T., AND SWAMI, A. 1993a. Database mining: A performance perspective. IEEE Trans. Knowledge and Data Eng. 5, 6 (Nov.), 914–925.
• AGRAWAL, R., IMIELINSKI, T., AND SWAMI, A. 1993b. Mining association rules between sets of items in massive databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data. ACM, Washington D.C., 207–216.
• BAYARDO, B. 1998. Efficiently mining long patterns from databases. In: Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data. ACM, Seattle, Washington, 85–93.
• BRIN, S., MOTWANI, R., AND SILVERSTEIN, C. 1997. Beyond market baskets: Generalizing association rules to correlations. In: Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data. ACM, Tucson, Arizona, 265–276.
• CARTER, C., HAMILTON, H., AND CERCONE, N. 1997. Share based measures for itemsets. In Principles of Data Mining and Knowledge Discovery. Springer, Trondheim, Norway, 14–24.
• CHEN, M., HAN, J., AND YU, P. 1996. Data mining: An overview from a database perspective. IEEE Trans. Knowledge and Data Eng. 8, 6 (Nov.), 866–881.
• HAN, J., PEI, J., AND YIN, Y. 2000. Mining frequent patterns without candidate generation. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. ACM, Dallas, Texas, 1–12.
• HUSSAIN, F., LIU, H., SUZUKI, E., AND LU, H. 2000. Exception rule mining with a relative interestingness measure. In: Proceedings of The Third Pacific Asia Conference on Knowledge Discovery and Data Mining, PADKK 2000. Springer, Kyoto, Japan, 86–97.
• HWANG, S., HO, S., AND TANG, J. 1999. Mining exception instances to facilitate workflow exception handling. In: Proceedings of the Sixth International Conference on Database Systems for Advanced Applications (DASFAA). IEEE Computer Society, Hsinchu, Taiwan, 45–52.
• LIU, H., LU, H., FENG, L., AND HUSSAIN, F. 1999. Efficient search of reliable exceptions. In: Proceedings of The Third Pacific Asia Conference on Knowledge Discovery and Data Mining, PADKK 1999. Springer, Beijing, China, 194–204.
• PADMANABHAN, B. AND TUZHILIN, A. 1998. A belief-driven method for discovering unexpected patterns. In: Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98). AAAI, Newport Beach, California, USA, 94–100. ACM Transactions on Information Systems, Vol. 22, No. 3, July 2004.Efficient Mining of Both Positive and Negative Association Rules * 405
• PADMANABHAN, B. AND TUZHILIN, A. 2000. Small is beautiful: discovering the minimal set of unexpected patterns. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, Boston, MA, USA, 54–63.
• PARK, J., CHEN, M., AND YU, P . 1997. Using a hash-based method with transaction trimming for mining association rules. IEEE Trans. Knowl. Data Eng. 9, 5 (Sept.), 813–824.
• PIATETSKY-SHAPIRO, G. 1991. Discovery, analysis, and presentation of strong rules. In Knowledge discovery in Databases. AAAI/MIT, Menlo Park, Calif., USA, 229–248.
• SAVASERE, A., OMIECINSKI, E., AND NAVATHE, S. 1998. Mining for strong negative associations in a large database of customer transactions. In: Proceedings of the Fourteenth International Conference on Data Engineering. IEEE Computer Society, Orlando, Florida, 494–502.
• SHINTANI, T. AND KITSUREGAWA, M. 1998. Parallel mining algorithms for generalized association rules with classification hierarchy. In: Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data. ACM, Seattle, Washington, 25–36.
• SHORTLIFFE, E. 1976. Computer Based Medical Consultations: MYCIN. Elsevier, New York.
• SRIKANT, R. AND AGRAWAL, R. 1996. Mining quantitative association rules in large relational tables. In: Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data. ACM, Montreal, Quebec, Canada, 1–12.
• SRIKANT, R. AND AGRAWAL, R. 1997. Mining generalized association rules. Future Generation Computer Systems 13, 2-3 (Nov.), 161–180.
• SUZUKI, E. 1997. Autonomous discovery of reliable exception rules. In: Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD-97). AAAI, Newport Beach, California, USA, 259–262.
• SUZUKI, E. AND SHIMURA, M. 1996. Exceptional knowledge discovery in databases based on information theory. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96). AAAI, Portland, Oregon, USA, 275–278.
• TAN, P., KUMAR, V., AND SRIVASTAVA, J . 2000. Indirect association: Mining higher order dependencies in data. In Principles of Data Mining and Knowledge Discovery. Springer, Lyon, France, 632–637.
• TAN, P. AND KUMAR, V. 2002. Mining indirect associations in web data. In WEBKDD 2001 — Mining Web Log Data Across All Customers Touch Points, Third International Workshop, San Francisco, CA, USA, August 26, 2001, Revised Papers. Springer, San Francisco, CA, 145–166.
• TSUR, D., ULLMAN, J., ABITEBOUL, S., CLIFTON, C., MOTWANI, R., NESTOROV, S., AND ROSENTHAL, A. 1998. Query flocks: A generalization of association-rule mining. In: Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data. ACM, Seattle, Washington, USA, 1–12.,

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2004 EfficientMineofBothPosandNegAssocRules	Xindong Wu Chengqi Zhang Shichao Zhang			Efficient Mining of Both Positive and Negative Association Rules			http://www-staff.it.uts.edu.au/~zhangsc/scpaper/ACMIS.pdf