Data Mining Techniques

Part of the Management for Professionals book series (MANAGPROF)


Data mining, as already noted, is a component of the knowledge discovery process. It can be defined as a set of techniques that allows data analysis and exploration in order to discover significant rules or hidden models within large archives by means of an entirely or partially automated procedure (Berry and Linoff 1997).


Genetic Algorithm Fuzzy Logic Statistical Unit Data Mining Technique Fuzzy Logic System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Adriaans, P., & Zantinge, D. (1996). Data mining. Harlow: Addison-Wesley.Google Scholar
  2. Arthur, B., Holland, J., Palmer, R., & Tayler, P. (1991). Using genetic algorithms to model the stock market. In Proceedings of the forecasting and optimization in financial services conference, IBC Technical Services Ltd., London.Google Scholar
  3. Barrow, D. (1992). Making money with genetic algorithms. Proceedings of the fifth European seminar on neural network and genetic algorithms. London: IBC international services.Google Scholar
  4. Berry, M., & Linoff, G. (1997). Data mining techniques. New York: Wiley.Google Scholar
  5. Berson, A., Smith, S., & Thearling, K. (2000). Building data mining applications for CRM. New York: Datamanagement, Osborne, McGraw-Hill.Google Scholar
  6. Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Bertmon: Wadsworth Inc.Google Scholar
  7. Dayhoff, J. (1990). Neural network architectures: An introduction. New York: Van Nostrand Reinhold.Google Scholar
  8. Deboeck, G. J. (Ed.). (1995). Trading on the edge. New York: Wiley.Google Scholar
  9. Del Ciello, N., Dulli, S., & Saccardi, A. (2000). Metodi di data mining per il customer relationship management. Milano: FrancoAngeli.Google Scholar
  10. Dubois, D., & Prada, H. (1980). Fuzzy sets and systems: Theory and application. San Diego: Academic.Google Scholar
  11. Edelstein, H. (2000). Building profitable customer relationships with data mining. SPSS White Paper-Executive Briefing, pp. 1–13.Google Scholar
  12. Goldberg, D. E. (1989). Genetic algorithms in search optimization and machine learning. Menlo Park: Addison-Wesley.Google Scholar
  13. Holland, J. H. (1973). Genetic algorithms and the optimal allocation of trials. SIAM Journal on Computing, 2(2), 88–105.CrossRefGoogle Scholar
  14. Holland, J. H. (1984). Genetic algorithms and adaptation. Adaptive control of ill-defined systems (pp. 317–333). New York: Plenum Press.CrossRefGoogle Scholar
  15. Holland, J. H. (1987). Genetic algorithms and classifier systems: Foundations and future directions. In J. J. Grefenstette (Ed.), Genetic algorithms and their applications: Proceedings of the second international conference on genetic algorithms. London: Lawrence Erlbaum Associates.Google Scholar
  16. Holland, J. H. (1993a). Adaption in natural and artificial systems. Cambridge: Mit Press.Google Scholar
  17. Kirkpatrick, S., Gelatt, C. D., Jr., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671–680.CrossRefGoogle Scholar
  18. Kimball, R. (1996). Data warehouse toolkit. New York: Wiley.Google Scholar
  19. Kingdon, J., & Feldman, K. (1995). Intelligent techniques applied in finance. In C. Nottola & C. Rossignoli (Eds.), Intelligenza artificiale in banca: tendenze evolutive ed esperienze operative a confronto. Milano: FrancoAngeli.Google Scholar
  20. Kosko, B. (1992). Neural networks and fuzzy systems. A dynamical systems approach to machine intelligence. Englewood Cliffs: Prentice-Hall.Google Scholar
  21. Kosko, B. (1996). Fuzzy engineering. Englewood Cliffs: Prentice-Hall.Google Scholar
  22. Nottola, C., & Rossignoli, C. (Eds.). (1995). Intelligenza artificiale in banca: tendenze evolutive ed esperienze operative a confronto. Milano: FrancoAngeli.Google Scholar
  23. Rossignoli, C. (1993). Applicazioni di sistemi esperti e reti neurali un campo finanziario. Milano: FrancoAngeli.Google Scholar
  24. Rossignoli, C. (1997). Organizzazione e sistemi informativi. Milano: FrancoAngeli.Google Scholar
  25. Rumelhart, D. E., & McClelland, J. L. (1986). Parallel distributed processing: explorations in the microstructure of cognition. Volume 1: Foundations. Cambridge: MIT Press.Google Scholar
  26. Stein, J. (1991). Neural networks: From the chalkboard to the trading room. New York: Trading Techniques.Google Scholar
  27. Tarun, K. (1990). Foundations of neural networks. New York: Addison-Wesley.Google Scholar
  28. Taylor, F. W. (1911). Scientific management. New York: Harper & Row.Google Scholar
  29. Turban, E., & Trippi, R. (1993). Neural networks in finance and investing: Using artificial intelligence to improve real-world performance. New York: Probus.Google Scholar
  30. Unwin, C., & Cogbill, S. (1991). Artificial intelligence in financial trading. New York: Financial trading International.Google Scholar
  31. Valino, J., Rubio, R., & Villaverde, R. F. (1989). Credit card evaluation system based on neural computing. Lugano: IDSIA.Google Scholar
  32. Wasserman, P. D. (1989). Neural computing: Theory and practice. New York: Van Nostrand Reinhold.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Faculty of Economics and Management CeTIF - Research Center on Innovation and Financial InstitutionsUniversità Cattolica del Sacro CuoreMilanoItaly

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