RainForest—A Framework for Fast Decision Tree Construction of Large Datasets

• Agrawal, R., Ghosh, S., Imielinski, T., Iyer, B., and Swami, A. 1992. An interval classifier for database mining applications. In Proc. of the VLDB Conference. Vancouver, British Columbia, Canada, pp. 560–573.

• Agrawal, R., Imielinski, T., and Swami, A. 1993. Database mining: A performance perspective. IEEE Transactions on Knowledge and Data Engineering, 5(6):914–925.

  Article  Google Scholar 

• Agresti, A. 1990. Categorical Data Analysis. John Wiley and Sons.

• Astrahan, M.M., Schkolnick, M., and Whang, K.-Y. 1987. Approximating the number of unique values of an attribute without sorting. Information Systems, 12(1):11–15.

  Article  Google Scholar 

• Brachman, R.J., Khabaza, T., Kloesgen, W., Shapiro, G.P., and Simoudis, E. 1996. Mining business databases. Communications of the ACM, 39(11):42–48.

  Article  Google Scholar 

• Bishop, C.M. 1995. Neural Networks for Pattern Recognition. New York, NY: Oxford University Press.

  Google Scholar 

• Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J. 1984. Classification and Regression Trees. Wadsworth: Belmont.

  Google Scholar 

• Brodley, C.E. and Utgoff, P.E. 1992. Multivariate versus univariate decision trees. Technical Report 8, Department of Computer Science, University of Massachussetts, Amherst, MA.

  Google Scholar 

• Catlett, J. 1991a. On changing continuos attributes into ordered discrete attributes. Proceedings of the European Working Session on Learning: Machine Learning, 482:164–178.

  MathSciNet  Google Scholar 

• Catlett, J. 1991b. Megainduction: Machine learning on very large databases. PhD Thesis, University of Sydney.

• Chan, P.K. and Stolfo, S.J. 1993a. Experiments on multistrategy learning by meta-learning. In Proc. Second Intl. Conference on Info. and Knowledge Mgmt., pp. 314–323.

• Chan, P.K. and Stolfo, S.J. 1993b. Meta-learning for multistrategy and parallel learning. In Proc. Second Intl. Workshop on Multistrategy Learning, pp. 150–165.

• Cheeseman, P. and Stutz, J. 1996. Bayesian classification (autoclass): Theory and results. In Advances in Knowledge Discovery and Data Mining, U.M. Fayyad, G.P. Shapiro, P. Smyth, and R. Uthurusamy (Eds.). AAAI/MIT Press, ch. 6, pp. 153–180.

• Cheeseman, P., Kelly, J., Self, M., Stutz, J., Taylor,W., and Freeman, D. 1988. Autoclass: A bayesian classification system. In Proceedings of the Fifth International Conference on Machine Learning. Morgan Kaufmann.

• Cheng, J., Fayyad, U.M., Irani, K.B., and Qian, Z. 1988. Improved decision trees: A generalized version of ID3. In Proceedings of the Fifth International Conference on Machine Learning. Morgan Kaufman.

• Chirstensen, R. 1997. Log-Linear Models and Logistic Regression, 2nd ed. Springer.

• Corruble, V., Brown, D.E., and Pittard, C.L. 1993. A comparison of decision classifiers with backpropagation neural networks for multimodal classification problems. Pattern Recognition, 26:953–961.

  Article  Google Scholar 

• Curram, S.P. and Mingers, J. 1994. Neural networks, decision tree induction and discriminant analysis: An empirical comparison. Journal of the Operational Research Society, 45:440–450.

  Google Scholar 

• Dougherty, J., Kahove, R., and Sahami, M. 1995. Supervised and unsupervised discretization of continous features. In Machine Learning: Proceedings of the 12th International Conference, A. Prieditis and S. Russell (Eds.). Morgan Kaufmann.

• Fayyad, U.M. 1991. On the induction of decision trees for multiple concept learning. PhD Thesis, EECS Department, The University of Michigan.

• Fayyad, U., Haussler, D., and Stolorz, P. 1996. Mining scientific data. Communications of the ACM, 39(11).

• Fayyad, U.M. and Irani, K. 1993. Multi-interval discretization of continous-valued attributes for classification learning. In Proceedings of the 13th International Joint Conference on Artificial Intelligence. Morgan Kaufmann, pp. 1022–1027.

• Fayyad, U.M., Shapiro, G.P., Smyth, P., and Uthurusamy, R. (Eds.). 1996. Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press.

• Friedman, J.H. 1977. A recursive partitioning decision rule for nonparametric classifiers. IEEE Transactions on Computers, 26:404–408.

  MATH  Google Scholar 

• Fukuda, T., Morimoto,Y., and Morishita, S. 1996. Constructing efficient decision trees by using optimized numeric association rules. In Proceedings of the 22nd VLDB Conference. Mumbai, India.

• Garey, M.R. and Johnson, D.S. 1979. Computer and Intractability. Freeman and Company.

• Gillo, M.W. 1972. MAID: A honeywell 600 program for an automatised survey analysis. Behavioral Science, 17:251–252.

  Google Scholar 

• Goldberg, D.E. 1989. Genetic Algorithms in Search, Optimization and Machine Learning. Morgan Kaufmann.

• Graefe, G., Fayyad, U., and Chaudhuri, S. 1998. On the efficient gathering of sufficient statistics for classification from large SQL databases. In Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining. AAAI Press, pp. 204–208.

• Haas, P.J., Naughton, J.F., Seshadri, S., and Stokes, L. 1995. Sampling-based estimation of the number of distinct values of an attribute. In Proceedings of the Eighth International Conference on Very Large Databases (VLDB). Zurich, Switzerland, pp. 311–322.

• Hand, D.J. 1997. Construction and Assessment of Classification Rules. Chichester, England: John Wiley & Sons.

  Google Scholar 

• Hyafil, L. and Rivest, R.L. 1976. Constructing optimal binary decision trees is NP-complete. Information Processing Letters, 5(1):15–17.

  Article  MathSciNet  Google Scholar 

• Ibarra, O.H. and Kim, C.E. 1975. Fast approximation algorithms for the knapsack and sum of subsets problem. Journal of the ACM, 22:463–468.

  Article  MathSciNet  Google Scholar 

• Inman, W.H. 1996. The data warehouse and data mining. Communications of the ACM, 39(11).

• James, M. 1985. Classification Algorithms. Wiley.

• Kerber, R. 1991. Chimerge discretization of numeric attributes. In Proceedings of the 10th International Conference on Artificial Intelligence, pp. 123–128.

• Kohavi, R. 1995. The power of decision tables. In Proceedings of the 8th European Conference on Machine Learning. N. Lavrac and S. Wrobel (Eds.). Lecture Notes in Computer Science, vol. 912, Springer.

• Kohonen, T. 1995. Self-Organizing Maps. Heidelberg: Springer-Verlag.

  Google Scholar 

• Lim, T.-S., Loh, W.-Y., and Shih, Y.-S. 1997. An empirical comparison of decision trees and other classification methods. Technical Report 979, Department of Statistics, University of Wisconsin, Madison.

  Google Scholar 

• Liu, H. and Setiono, R. 1996. Chi2: Feature selection and discretization of numerical attributes. In Proceedings of the IEEE Tools on AI.

• Loh, W.-Y. and Shih, Y.-S. 1997. Split selection methods for classification trees. Statistica Sinica, 7(4):815–840.

  MathSciNet  Google Scholar 

• Loh, W.-Y. and Vanichsetakul, N. 1988. Tree-structured classification via generalized disriminant analysis (with discussion). Journal of the American Statistical Association, 83:715–728.

  MathSciNet  Google Scholar 

• Maass, W. 1994. Efficient agnostic pac-learning with simple hypothesis. In Proceedings of the Seventh Annual ACM Conference on Computational Learning Theory, pp. 67–75.

• Magidson, J. 1989. CHAID, LOGIT and log-linear modeling. Markting Information Systems, Report 11–130.

• Magidson, J. 1993a. The CHAID approach to segmentation modeling. In Handbook of Marketing Research, R. Bagozzi (Ed.). Blackwell.

• Magidson, J. 1993b. The use of the new ordinal algorithm in CHAID to target profitable segments. Journal of Database Marketing, 1(1).

• Mehta, M., Agrawal, R., and Rissanen, J. 1996. SLIQ: A fast scalable classifier for data mining. In Proc. of the Fifth Int'l Conference on Extending Database Technology (EDBT), Avignon, France.

• Mehta, M., Rissanen, J., and Agrawal, R. 1995. MDL-based decision tree pruning. In Proc. of the 1st Int'l Conference on Knowledge Discovery in Databases and Data Mining, Montreal, Canada.

• Michie, D., Spiegelhalter, D.J., and Taylor, C.C. 1994a. Machine Learning, Neural and Statistical Classification. Ellis Horwood.

• Michie, D., Spiegelhalter, D.J., and Taylor, C.C. (Eds.). 1994b. Machine Learning, Neural and Statistical Classification. London: Ellis Horwood.

  Google Scholar 

• Morgan, J.N. and Messenger, R.C. 1973. Thaid: A sequantial search program for the analysis of nominal scale dependent variables. Technical Report, Institute for Social Research, University of Michigan, Ann Arbor, Michigan.

  Google Scholar 

• Morimoto, Y., Fukuda, T., Matsuzawa, H., Tokuyama, T., and Yoda, K. 1998. Algorithms for mining association rules for binary segmentations of huge categorical databases. In Proceedings of the 24th International Conference on Very Large Databases (VLDB). Morgan Kaufmann.

• Murphy, O.J. and McCraw, R.L. 1991. Designing storage efficient decision trees. IEEE Trans. on Comp., 40(3):315–319.

  Article  Google Scholar 

• Murthy, S.K. 1995. On growing better decision trees from data. PhD Thesis, Department of Computer Science, Johns Hopkins University, Baltimore, Maryland.

  Google Scholar 

• Naumov, G.E. 1991. NP-completeness of problems of construction of optimal decision trees. Soviet Physics, Doklady, 36(4):270–271.

  MATH  MathSciNet  Google Scholar 

• Quinlan, J.R. 1979. Discovering rules by induction from large collections of examples. In Expert Systems in the Micro Electronic Age, D. Michie (Ed.). Edinburgh University Press: Edinburgh, UK.

  Google Scholar 

• Quinlan, J.R. 1983. Learning efficient classification procedures. In Machine Learning: An Artificial Intelligence Approach, T.M. Mitchell, R.S. Michalski, and J.G. Carbonell (Eds.). Palo Alto, CA: Tioga Press.

  Google Scholar 

• Quinlan, J.R. 1986. Induction of decision trees. Machine Learning, 1:81–106.

  Google Scholar 

• Quinlan, J.R. 1993. C4.5: Programs for Machine Learning. Morgan Kaufman.

• Rastogi, R. and Shim, K. 1998. PUBLIC: A decision tree classifier that integrates building and pruning. In Proceedings of the 24th International Conference on Very Large Databases. New York City, New York, pp. 404–415.

• Ripley, B.D. 1996. Pattern Recognition and Neural Networks. Cambridge: Cambridge University Press.

  Google Scholar 

• Rissanen, J. 1989. Stochastic Complexity in Statistical Inquiry. World Scientific Publ. Co.

• Sahni, S. 1975. Approximate algorithms for the 0/1 knapsack problem. Journal of the ACM, 22:115–124.

  Article  MATH  MathSciNet  Google Scholar 

• Sarle, W.S. 1994. Neural networks and statistical models. In Procedings of the Nineteenth Annual SAS Users Groups International Conference. SAS Institute, Inc., Cary, NC, pp. 1538–1550.

  Google Scholar 

• Shafer, J., Agrawal, R., and Mehta, M. 1996. SPRINT: A scalable parallel classifier for data mining. In Proc. of the 22nd Int'l Conference on Very Large Databases. Bombay, India.

• Shavlik, J.W., Mooney, R.J., and Towell, G.G. 1991. Symbolic and neural learning algorithms: An empirical comparison. Machine Learning, 6:111–144.

  Google Scholar 

• Sonquist, J.A., Baker, E.L., and Morgan, J.N. 1971. Searching for structure. Technical Report, Institute for Social Research, University of Michigan, Ann Arbor, Michigan.

  Google Scholar 

• Weiss, S.M. and Kulikowski, C.A. 1991. Computer Systems that Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning, and Expert Systems. Morgan Kaufman.

• Zighed, D.A., Rakotomalala, R., and Feschet, F. 1997. Optimal multiple intervals discretization of continous attributes for supervised learning. In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, pp. 295–298.

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