1Brin, S., Motwani, R., and Silvemtein, C. (1997a). Beyond Market Baskets: Generalizing Association Rules to Correlations. In Proceedings of the ACM SIGMOD International Conference on Management of Data (ACM SIGMOD 97), Pages 265-276.
2Brin, S., Motwani, R., Ullman, J. D., and Tsur, S. (1997b). Dynamic Itemset Counting and Implication Rules for Market Basket Data. In Proceedings of the ACM SIGMOD International Conference on Management of Data (ACM SIGMOD 97), Pages 265-276.
3English, L P. (1999). Improving Data Warehouse and Business Information Quality: Methods for Reducing Costs and Increasing profits. John Wiley & Sons, New York, USA.
4Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. (1996). The KDD Process for Extracting Useful Knowledge from Volumes of Data. Communications of the ACM, 39(11):27-34.
5Gersten, W., Wirth, R., and Arndt, D. (2000). Predictive Modeling in Automotive Direct Marketing: Tools, Experiences and Open Issues. In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 00), Pages 398-406, Boston, MA USA.
6Han, J., Pei, J., and Yin, Y. (2000). Mining Frequent Patterns without Candidate Generation. In Pmceedings of the 2000 ACM-SIGMOD International Confenerence on Management of Data, Dallas, Texas, USA.
7Handley, S., Langley, P., and Rauscher, F. A. (1998). Learn ing to Predict the Duration of an Automobile Trip. In Proceedings of 1998 International Conference on KDD and Data Mining (KDD98), Pages 219-223, New York City, USA.
8Hipp, J., Guntzer, U., and Grimmer, U. (2001). Integrating Association Rule Mining Algorithms with Relational Database Systems.In Proceedings of the International Conference on Enterprise Information Systems (ICEIS2001),Set'ubal, Portuga.
9Hipp, .1., G untzer, U., and Nakhaeizadeh, G. (2000a). Algo Rithms for Association Rule Mining - a General Survey and Comparison. SIGKDD Explorations, 2(1):58--64.
10Hipp, J., G untzer, U., and Nakhaeizadeh, G. (2000b). Mining Association Rules: Deriving a Superior Algorithm by Analysing Today's Approaches. In Proceedings of the 4th European Symposium on Principles of Data Mining and Knowledge Discovery (PKDD 00), Pages 159-168, Lyon, France.
