參考文獻 |
[AHM+97]Agrawal, R., Ho, C. T., Megiddo, N. and Srikant, R., Range queries in OLAP data cubes, Proc. ACM SIGMOD 97, pp73-88.
[AIS93]R. Agrawal, T. Imielinski, and A. Swami, “Mining association rules between sets of items in large database,” SIGMOD 93, pp. 207-216.
[AS96]R. Agrawal, and R. Srikant, “Mining quantitative association rules in large relational tables,” SIGMOD 96, pp. 1-12.
[BDF97]Barbara, D., DuMouchel, W., Faloutsos, C., Haas, P. J., Hellerstein, J. H., Ioannidis, Y., Jagadish, H. V., Johnson, T., Ng, R., Poosala, V., Ross, K. A., and Servcik, K. C., The New Jersey data reduction report. Bulletion of the Technical Committee on Data Engineering, 20:3-45, Dec. 1997.
[Cat91]Catlett, J., Megainduction: Machine Learning on Very Large Database. Ph.D. Thesis, University of Sydney, 1991.
[CCH92]Cai, Y., Cercone, N., and Han, J., “Knowledge discovery in databases: an attribute-oriented approach,” VLDB 1992, pp. 547-559.
[CCS93]Codd, E. F., Codd, S. B., and Salley, C. T., “Beyond decision support,” Computer World,” 27, July 1993.
[CD97]Chaudhuri, S., and Dayal, U., “An overview of data warehousing and OLAP technology,” ACM SIGMOD Record, 26:65-74, 1997.
[CPY95]Chen, M.-S, Park, J.-S., and Yu, P. S., “An effective hash based algorithm for mining association rules,” Proc. ACM SIGMOD, pp. 175-186, May 1995.
[DP97]Devore, J. and Peck, R., Statistics: The Exploration and Analysis of Data. New York: Duxbury Press, 1997.
[FI93]Fayyad, U. and Irani, K., Multi-interval discretizaion of continuous-values attributes for classification learning. In Proc. 13th Intl. Joint Conf. On Artificial Intelligence(IJCAI’93), pages 1022-1029, Chambery, France, 1993.
[FMMT96]Fukuda, T., Morimoto, Y., Morishita, S., and Tokuyama, T., “Data mining using two-dimensional optimized association rules: Scheme, algorithms, and visualization,” In Proc. of the ACM SIGMOD Conference on Management of Data, June 1996.
[FPS96]U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, “The KDD process for extracting useful knowledge from volumes of data,” IEEE Transactions on Knowledge and Data Engineering, 8(6): pp.866-883, 1996.
[GCB97]Gray, J., Chaudhuri, S., Bosworth, A., Layman, A., Reichart, D., Venkatrao, M., Pellow, F., and Pirahesh, H., “Data cube: a relational aggregation operator generalizing group-by, cross-tab and sub-totals,” Data Mining and Knowledge Discovery,” l:29-54, 1997.
[HK01]J. Han and M. Kamber, Data mining: Concepts and Techniques, Academic Press, 2001.
[KFW98]Kuok, C. M.., Fu, A., Wong, M. H., :Mining fuzzy assocaiation rules in databases,” SIGMOD Record — Quarterly Publication of the Special Interest Group on Management Data v.27 n.1 pp 41-46, 1998.
[LS95]Liu, H. and Setiono, R., Chi2: Feature selection and discretization of numeric attributes. In Proc. 7th IEEE Intl. Conf. Tools with AI(ICTAI’95), pages 388-391, Los Alamitos, CA:IEEE Computer Society, 1995.
[Qui93]Quinlan, J. R., C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann, 1993.
[RS97]Rastogi, R. and Shim, K., “Mining optimized association rules for numeric attributes,” Technical Report 0112370971110-25, Bell Laboratories, Murray Hill, 1997.
[RS99]Rastogi, R. and Shim, K., “Mining optimized support rules for numeric attributes,” In Proc. of the 15th International Data Engineering Conference, pp 512-521. |