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Data Mining in Economics, Finance, and Marketing

Part of the Lecture Notes in Computer Science book series (LNAI,volume 2049)

Abstract

Data Mining has become a buzzword in industry in recent years. It is something that everyone is talking about but few seem to understand. There are two reasons for this lack of understanding: First is the fact that Data Mining researchers have very diverse backgrounds such as machine learning, psychology and statistics. This means that the research is often based on different methodologies and communication links e.g. notation is often unique to a particular research area which hampers the exchange of ideas and the dissemination to the wider public. The second reason for the lack of understanding is that the main ideas behind Data Mining are often completely opposite to mainstream statistics and as many companies interested in Data Mining already employ statisticians, such a change of view can create opposition.

Keywords

  • Data Mining
  • Mobile Agent
  • Data Mining Problem
  • Data Mining Research
  • Mobile Agent Technology

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.

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References

  1. Adamidis, P, and Koukoulakis, K. Evolutionary Data Mining applied to TV Databases: A First Approach. In [15].

    Google Scholar 

  2. Coenen, F., Swinnen, G., Vanhoof, K. and Wets, G. The Improvement of Response Modelling: Combining Rule-Induction and Case-Based Reasoning. In [15].

    Google Scholar 

  3. Dikaiakos, M. FIGI: Using Mobile Agent Technology to Collect Financial Information on Internet. In [15].

    Google Scholar 

  4. Feelders, A. and Daniels, H. Discovery in practice. In [15].

    Google Scholar 

  5. Horizon Systems Laboratory. Mobile Agent Computing. A white paper. Mitsubishi Electric ITA., January 1998.

    Google Scholar 

  6. Karamanlidou, M. Tuffier, O. and Vlahavas, I. Stock Miner: A System for Knowledge Discovery in Financial Data. In [15].

    Google Scholar 

  7. Krone, A. and Kiendl, H. Rule-based decision analysis with Fuzzy-ROSA method, Proceedings of EFDAN’96, Dortmund (Germany), 1996, 109–114.

    Google Scholar 

  8. Kowalczyk, W., Piasta, Z. Rough-set inspired approach to knowledge discovery in business databases. In: X. Wu, R. Kotagiri, K. R. Korb, Research and Development in Knowledge Discovery and Data Mining, Proceedings of the Second Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD-98, Melburne, 15–17 April, Springer-Verlag, Berlin, Heidelberg, New York, 1998, 186–197.

    Google Scholar 

  9. MIT GmbH. WINROSA: Handbook, Aachen, Germany, 1997(b).

    Google Scholar 

  10. Piasta, Z., Lenarcik, A. Learning rough classifiers from large databases with missing values. In: L. Polkowski, A. Skowron, (eds), Rough Sets in Knowledge Discovery, Physica Verlag, 1998, 483–499.

    Google Scholar 

  11. Piasta, Z. Analyzing business databases with the ProbRough rule induction system. In [15].

    Google Scholar 

  12. Quinlan, J.R., C4.5: Programs for Machine Learning, Morgan Kaufmann, 1993.

    Google Scholar 

  13. Nikolaos Thomaidis, George Dounias, Costas D. Zopounidis: A fuzzy rule based learning method for corporate bankruptcy prediction. In [15].

    Google Scholar 

  14. Van den Poel, D., Piasta, Z. Purchase prediction in database marketing with the ProbRough system. In: L. Polkowski, A. Skowron, (eds), Rough Sets and Current Trends in Computing, Physica Verlag, 1998, 593–600.

    Google Scholar 

  15. Proceedings of the Workshop on Data Mining in Economics, Finance and Marketing, Advanced Course on Artificial Intelligence (ACAI’ 99), Chania, Greece, 1999 (http://www.iit.demokritos.gr/skel/eetn/acai99/Workshops.htm).

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© 2001 Springer-Verlag Berlin Heidelberg

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Jessen, H.C., Paliouras, G. (2001). Data Mining in Economics, Finance, and Marketing. In: Paliouras, G., Karkaletsis, V., Spyropoulos, C.D. (eds) Machine Learning and Its Applications. ACAI 1999. Lecture Notes in Computer Science(), vol 2049. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44673-7_18

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  • DOI: https://doi.org/10.1007/3-540-44673-7_18

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42490-1

  • Online ISBN: 978-3-540-44673-6

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