References

---

Agrawal, R.,Faloutsos, C., & Swami, A. (1993, October). Efficient Similarity Search in Sequence Databases. Proceedings of 4^th International Conference Foundations of Data Organization and Algorithms.

Agrawal, R.,Ghosh, S.,Imielinski, I. B., & Swami, A. (1992, August). An Interval Classifier for Database Mining Applications. Proceedings of 18^th International Conference on Very Large DataBases, (pp. 560-573).

Agrawal, R.,Imielinski, S., & Swami, A. (1993a, December). Database Mining: A Performance Perspective. IEEE Transactions on Knowledge and Data Engineering, (pp. 914-925).

Agrawal, R.,Imielinski, S., & Swami, A. (1993b, May). Mining Association Rules between Sets of Items in Large Databases. Proceedings ACM SIGMOD, (pp. 207-216).

Agrawal, R.,Lin, K. I.,Sawhney, H.S., & Shim, K. (1995, September). Fast Similarity Search in the presence of Noise, Scaling, and Translation in Time Series Databases. Proceedings of 21^st International Conference on Very Large DataBases, (pp. 490-501).

Agrawal, R.,Mehta, M.,Shafer, J.,Srikant, R.,Arning, A., & Bollinger, T. (1996, August). The Quest Data Mining System. Proceedings of International Conference of Data Mining and Knowledge Discovery (KDD 96), (pp. 244-249) Portland, OR.

Agrawal, R. & Srikant, R. (1994). Fast Algorithms for Mining Association Rules in Large Databases. Proceedings of 20th International Conference on Very Large Databases, (pp. 478-499).

Agrawal, R. & Srikant, R. (1995, March). Mining Sequential Patterns. Proceedings of 11^th International Conference on Data Engineering, (pp. 3-14).

Altman, E.L.,Eisenbeis, R.A., & Sinkey, J. (1981). Application of Classification Techniques in Business, Banking and Finance. Greenwich, CT: JAI Press.

Atlas, L.,Cole, R.,Connor, J.,El-Sharkawi, M.,Marks II, R.J.,Muthusamy, Y., & Barnard, E. (1990). Performance Comparisons between Back propagation Networks and Classification Trees on Three Real-World Applications. Advances in Neural Information Processing Systems, 2, Denver, CO.

Banker, R.D. & Slaughter, S.A. (1997, December). A Field Study of Scale Economies in Software Maintenance. Management Science, 43(12), 1709-1725.

Becker, S. & Cun, Y.L. (1998). Improving the Convergence of Back-Propagation Learning with Second-Order Methods. Proceedings 1988 Connectionist Models Summer School, Morgan-Kaufman, San Mateo, CA.

Bhattacharyya, S. & Pendharkar, P.C. (1998). Inductive Evolutionary and Neural Techniques for Discrimination. Decision Sciences, 29(4), 871-899.

Bhattacharyya, S.,Ratner, B., & Omac, B. (1995). DecileMax. Bruce Ratner and Associates.

Breiman, L.,Friedman, J.H.,Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Monterey, CA: Wadsworth and Brooks.

Cheeseman, P. & Stutz, J. (1996). Bayesian Classification (AutoClass): Theory and Results. In U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, & R. Uthurusamy, (Eds.), Advances in Knowledge Discovery and Data Mining, (pp. 153-180), AAAI/MIT Press.

Chen, M. S.,Han, J., & Yu, P.S. (1996, December). Data Mining: An Overview from Database Perspective. IEEE Transactions on Knowledge and Data Engineering, 8(2), 866-883.

Chen, M.S.,Park, J.S., & Yu, P.S. (1996). Data Mining for Path Traversal Patterns in a Web Environment. Proceedings of 16^th International Conference of Distributed Computing Systems, (May 27-30, pp. 385-392).

Cheung, D.W.,Ng, V. T.,Fu, A.W., & Fu, Y. (1996, December). Efficient Mining of Association Rules in Distributed Databases. IEEE Transactions on Knowledge and Data Engineering, 8(6), 911-922.

Chung, H. M. & Silver, M.S. (1992). Rule-Based Expert Systems and Linear Models: An Empirical Comparison of Learning-By-Examples Methods. Decision Sciences, 23, 687-707.

Clark, P. & Niblett, T. (1989). The CN2 Induction Algorithm. Machine Learning, 3(4), 261-283.

Curry, B. & Morgan, P. (1997). Neural Networks: A Need for Caution. Omega: An International Journal of Management Science, 25(1), 123-133.

Duda, R.O. & Hart, P.E. (1973). Pattern Classification and Scene Analysis. New York: John Wiley & Sons.

Ehrenfeucht, A.,Haussler, D.,Kearns, M., & Valiant, L. (1989). A General Lower Bound on the Number of Examples needed for Learning. Information and Computation, (pp. 247-261).

Ester, M.,Kriegel, H.P., & Xu, X. (1995, August). Knowledge Discovery in Large Spatial Databases: Focusing Techniques for Efficient Class Identification. Proceedings Fourth International Symposium of Large Spatial Databases, (pp. 67-82) Portland, ME.

Falhman, S. E. (1988). Faster-Learning Variations of Back-Propagation: An Empirical Study. Proceedings 1988 Connectionist Models Summer School, (pp. 38-51) San Mateo, CA: Morgan-Kaufman.

Faloutsos, C. & Lin, K.I. (1995, May). FastMap: A Fast Algorithm for Indexing, Data-Mining and Visualization of Traditional and Multimedia Datasets. Proceedings ACM Sigmoid, (pp. 163-174).

Faloutsos, C.,Ranganathan, M., & Manolopoulos. (1994, May). Fast SubSequence Matching in Time-Series Databases. Proceedings ACM Sigmoid, (pp. 419-429) Minneapolis, MN.

Fisher, D. (1987, July). Improving Inference Through Conceptual Clustering. Proceedings AAAI Conference, (pp. 461-465) Seattle, WA.

Fisher, D. (1995, August). Optimization and Simplification of Hierarchical Clusterings. Proceedings First International Conference on Knowledge Discovery and Data Mining (KDD '95), (pp. 118-123) Montreal, Canada.

Fisher, D.H. & McKusick, K.B. (1989). An Empirical Comparison of ID3 and Back-Propagation. Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, (pp. 788-793) Detroit, MI: Morgan Kaufmann, San Mateo, CA.

Fisher, R.A. (1936). The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7, 179-188.

Flowers, S. (1996). Software Failure Management Failure: Amazing Stories and Cautionary Tales. New York: John Wiley and Sons.

Foss, B. W. (1993). Fast, Faster, Fastest Development. Computerworld, 27, 22, (pp. 81-83).

Freed, N. & Glover, F. (1981). A Linear Programming Approach to the Discriminant Problem. Decision Sciences, 12, 68-74.

Han, J. & Fu, Y. (1995, September). Discovery of Multiple-Level Association Rules from Large Databases. Proceedings of 21^st International Conference on Very Large Databases, (pp. 420-431).

Hand, D.J. (1981). Discrimination and Classification. New York: John Wiley & Sons.

Hansen, J.V,McDOnald, J.B., & Stice, J.D. (1992). Artificial Intelligence and Generalized Qualitative-Response Models: An Empirical Test on Two Audit Decision-Making Domains. Decision Sciences, 23, 708-723.

Jain, A.K. & Dubes, R.C. (1988). Algorithms for Clustering Data. Prentice-Hall.

Jain, B.A. & Nag, B.N. (1995). Artificial Neural Network Models for Pricing Initial Public Offerings. Decision Sciences, 26(3), 283-302.

King, R.D.,Henry, R.,Feng, C., & Sutherland, A. (1994). A Comparative Study of Classification Algorithms: Statistical, Machine Learning and Neural Net-work. In K. Furukawa, D. Michie & S. Muggleton (Eds.), Machine Intelligence, 13: Machine Intelligence and Inductive Learning. Oxford: Clarendon Press.

Koehler, G. J. (1991, Fall). Linear Discriminant Functions Determined through Genetic Search. ORSA Journal on Computing, 3(4), 345-357.

Koehler, G. J. & Erenguc, S.S. (1990). Minimizing Misclassifications in Linear Discriminant Analysis. Decision Sciences, 21, 63-85.

Koza, J.R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press.

Li, C.S.,Yu, P.S., & Castelli, V. (1996, February). Hierarchy Scan: A Hierarchical Similarity Search Algorithm for Databases of Long Sequences. Proceedings of 12^th International Conference on Data Engineering.

Lippmann, R.P. (1987, April). An Introduction to Computing with Neural Networks. IEEE ASSP Magazine, (pp. 4-22).

Mannila, H.,Toivonen, H., & Verkamo, I., A. (1994, July). Efficient Algorithms for Discovering Association Rules. Proceedings AAAI Workshop of Knowledge Discovery in Databases, (pp. 181-192).

Mannino, M.V. & Koushik, M.V. (1996). The Cost Minimizing Inverse Classification Problem: A Genetic Algorithm Approach. Research Paper, University of Washington, Seattle, WA.

Mookerjee, V. & Dos Santos, B. (1993, August). Inductive Expert System Design Maximizing System Value. Information Systems Research, 4(2), 111-140.

Mookerjee, V.S. & Mannino, M. V. (1997, March) Redesigning Case Retrieval to Reduce Information Acquisition Costs. Information Systems Research, 8(1), 51-68.

Murphy, C.K. & Benaroch, M. (1997, Fall). Adding Value to Induced Decision Trees for Time Sensitive Data. INFORMS Journal of Computing, 9(4).

Ng, R. & Han, J. (1994, September). Efficient and Effective Clustering Method for Spatial Data Mining. Proceedings of International Conference on Very Large DataBases, (pp. 144-155) Santiago, Chile.

Nunez, M. (1991). The Use of Background Knowledge in Decision Tree Induction. Machine Learning, 6, 231-250.

Park, J. (1997). Classifier Performance vs. Data Characteristics: Binary Tree Case. Proceedings of 2^nd INFORMS Conference of Information Systems and Technology, (pp. 1-8).

Park, J.S.,Chen, M.S., & Yu, P.S. (1995, May). Efficient Parallel Data Mining for Association Rules. Proceedings ACM SIGMOD, (pp. 175-186).

Parker, D.B. (1987). Optimal Algorithms for Adaptive Networks: Second Order Back-Propagation, Second Order Direct Propagation, and Second Order Hebbian Learning. Proceedings IEEE International Conference on Neural Networks, (pp. 593-600) San Diego, CA: IEEE Press.

Pass, S. (1997, October). Discovering Value in a Mountain of Data. OR/MS Today, (pp. 24-28).

Patuwo, E.,Hu, M.Y., & Hung, M.S. (1993). Two-Group Classification Using Neural Networks. Decision Sciences, 24(4), 825-845.

Pendharkar, P.C. & Kumar, S. (1998). A Data Envelopment Analysis (DEA) Application for Marginal Cost Assignment in Certain Case Based Expert Systems. 3^rd Proceedings of INFORMS Conference on Information Systems & Technology, (April 22-29) Montreal, Canada.

Pendharkar, P.C.,Rodger, J.A., & Kumar, S. (1997-1998, Winter). Operational Auditing and Testing in Client/Server Systems. Review of Accounting Information Systems, 2(1), 17-22.

Piramuthu, S.,Ragavan, H., & Shaw, M. (1998, March). Using Feature Construction to Improve the Performance of Neural Networks. Management Science, 44(3).

Quinlan, J.R. (1993). Comparing Connectionist and Symbolic Learning Methods. In S. Hanson, G. Drastal & R. Rivest (Eds.), Computational Learning Theory and Symbolic Learning Methods. Cambridge, MA: MIT Press.

Quinlan, J.R. (1986). Induction of Decision Trees. Machine Learning, 1, 81-106.

Radcliffe, N.J. & Surry, P.D. (1995). Fundamental Limitations on Search Algorithms: Evolutionary Computing in Perspective. In J. van Leeuwen (Ed.), Computer Science Today: Recent Trends and Developments. Lecture Notes in Computer Science, Volume 1000, Springer-Verlag.

Rosenblatt, F. (1962). Principle of Neurodynamics. Spartan, NY.

Rumelhart, D.E.,Hinton, G.E., & Williams, R.J. (1986). Learning Internal Representations by Error Propagation. In D.E. Rumelhart & J.L. McClelland (Eds.), Parallel Distributed Processing: Exploration in the Microstruc-ture of Cognition, Volume 1: Foundations, Cambridge, MA: MIT Press.

Savasere, A.,Omiecinski, E., & Navathe, S. (1995, September). An Efficient Algorithm for Mining Association Rules in Large Databases. Proceedings of 21^st International Conference on Very Large Databases, (pp. 432-444).

Schaffer, C. (1994). A Conservation Law for Generalization Performance. Proceedings of the Eleventh International Conference on Machine Learning, (pp. 259-265).

Shavlik, J.W.,Mooney, R.J., & Towell, G.G. (1991). Symbolic and Neural Learning Algorithms: An Experimental Comparison. Machine Learning, 6, 111-143.

Srikant, R. & Agrawal, R. (1995, September). Mining Generalized Association Rules. Proceedings of 21^st International Conference on Very Large Databases, (pp. 407-419).

Subramanian, G.H. & Zarnich, G.E. (1995-1996, Winter). An Examination of Some Software Development Effort and Productivity Determinants in ICASE Tool Projects. Journal of Management Information Systems.

Weiss, S.M. & Kapouleas, I. (1989). An Empirical Comparison of Pattern Recognition, Neural Nets, and Machine Learning Classification Methods. Proceedings of the 11th International Joint Conference on Artificial Intelligence, (pp. 688-693), Detroit, MI: Morgan Kaufmann, San Mateo, CA.

Wolpert, D.H. & Macready, W.G. (1995). No Free Lunch Theorems for Search. Santa Fe Institute Technical Report No. SFI-TR-95-02-010.

Zhang, T.,Ramakrishnan, & Livny, M. (1995, May). BIRCH: An Efficient Data Clustering method for Very Large Databases. Proceedings 1996 ACM SIGMOD International Conference on Management Data, (pp. 316-327) San Jose, CA.

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