- 1 Anand, T. Opportunity Explorer: Navigating large databases using knowledge discovery templates. J. Intell. Inf. Syst. 4, 1 (Jan. 1995), 27-38.Google ScholarCross Ref
- 2 Anand, T., and Kahn, G. Focusing knowledge-based techniques on market analysis. IEEE Expert 8, 4 (Aug. 1993), 19-24. Google ScholarDigital Library
- 3 Brachman, R., and Anand, T. The process of knowledge discovery in databases: A human-centered approach. In Advances in Knowledge Discovery and Data Mining, U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, Eds. AAAI Press/The MIT Press, Cambridge, Mass., 1996, pp. 37-57. Google ScholarDigital Library
- 4 Brachman, R., Selfridge, P., Terveen, L., Altman, B., Borgida, A., Halper, F., Kirk, T., Lazar, A., McGuinness, D., and Resnick, L. Integrated support for data archaeology. Int. J. Intell. Coop. Inf. Syst. 2, 2 (June 1993), 159-185.Google Scholar
- 5 Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy, R., Eds. Advances in Knowledge Discovery and Data Mining. AAAI Press/The MIT Press, Cambridge, Mass., 1996. Google ScholarDigital Library
- 6 Hall, J., Mani, G., and Barr, D. Applying computational intelligence to the investment process. In Proceedings of CIFER-96: Computational Intelligence in Financial Engineering. (New York, March 1996). IEEE Press, Piscataway, N J., 1996.Google Scholar
- 7 Khabaza, T., and Shearer, C. Data mining with Clementine. In the digest of the IEE Colloquium on Knowledge Discovery in Databases, Digest 1995/021(B) (London, Feb. 1995).Google Scholar
- 8 Kloesgen, W. Tasks, methods, and applications of knowledge extraction. In New Techniques and Technologies for Statistics, W. Kloesgen, P. Nanopoulos, and A. Unwin, Eds. IOS Press, Amsterdam, 1996, 163-182.Google Scholar
- 9 Mannila, H., Toivonen, H., and Verkamo, A.I. Discovering frequent episodes in sequences. In Proceedings of 1st International Conference on Knowledge Discovery and Data Mining (Montrdal, Aug. 1995). AAAI Press, Menlo Park, Calif., 1995, pp. 210-215.Google Scholar
- 10 Matheus, C., Piatetsky-Shapiro, G., and McNeill, D. Selecting and reporting what is interesting: The KEFIR application to healthcare data. In Advances in Knowledge Discovery and Data Mining, U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, Eds. AAAI Press/The MIT Press, Cambridge, Mass., 1996, pp. 495-516. Google ScholarDigital Library
- 11 Selfridge, P.G., Srivastava, D., and Wilson, L.O. IDEA: Interactive Data Exploration and Analysis. In Proceedings of SIGMOD- 96 (Montrdal, June 1996). ACM Press, New York, 1996, pp. 24-34. Google ScholarDigital Library
- 12 Senator, T.E., Goldberg, H.G., Wooten,J., Cottini, M.A., Khan, A.F.U., Klinger, C.D., Llamas, W.M., Marrone, M.P., and Wong, R.W.H. The Financial Crimes Enforcement Network M System (FMS): Identifying potential money laundering from reports of large cash transactions. AIMag. 16, 4 (Winter 1995), 21-39.Google Scholar
Index Terms
- Mining business databases
Recommendations
Efficient algorithms for mining high-utility itemsets in uncertain databases
High-utility itemset mining (HUIM) is a useful set of techniques for discovering patterns in transaction databases, which considers both quantity and profit of items. However, most algorithms for mining high-utility itemsets (HUIs) assume that the ...
Mining frequent itemsets in large databases: The hierarchical partitioning approach
Although many methods have been proposed to enhance the efficiencies of data mining, little research has been devoted to the issue of scalability - that is, the problem of mining frequent itemsets when the size of the database is very large. This study ...
Comments