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Mining Changes for Real-Life Applications

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Data Warehousing and Knowledge Discovery (DaWaK 2000)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1874))

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Abstract

Much of the data mining research has been focused on devising techniques to build accurate models and to discover rules from databases. Relatively little attention has been paid to mining changes in databases collected over time. For businesses, knowing what is changing and how it has changed is of crucial importance because it allows businesses to provide the right products and services to suit the changing market needs. If undesirable changes are detected, remedial measures need to be implemented to stop or to delay such changes. In many applications, mining for changes can be more important than producing accurate models for prediction. A model, no matter how accurate, can only predict based on patterns mined in the old data. That is, a model requires a stable environment, otherwise it will cease to be accurate. However, in many business situations, constant human intervention (i.e., actions) to the environment is a fact of life. In such an environment, building a predictive model is of limited use. Change mining becomes important for understanding the behaviors of customers. In this paper, we study change mining in the contexts of decision tree classification for real-life applications.

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References

  1. Agrawal, R. and Psaila, G. “Active data mining” KDD-95, 1995.

    Google Scholar 

  2. Agrawal, R., Imielinski, T., Swami, A. “Mining association rules between sets of items in large databases” SIGMOD-1993, 1993, pp. 207–216.

    Google Scholar 

  3. Cheung, D. W., Han, J, V. Ng, and Wong, C.Y. “Maintenance of discovered association rules in large databases: an incremental updating technique” ICDE-96, 1996.

    Google Scholar 

  4. Dong, G. and Li, J. “Efficient mining of emerging patterns: discovering trends and differences.” KDD-99, 1999.

    Google Scholar 

  5. Freund, Y and Mansour, Y. “Learning under persistent drift” Computational learning theory: Third European conference, 1997.

    Google Scholar 

  6. Ganti, V., Gehrke, J., and Ramakrishnan, R. “A framework for measuring changes in data characteristics” POPS-99.

    Google Scholar 

  7. Helmbold, D. P. and Long, P. M. “Tracking drifting concepts by minimizing disagreements.” Machine Learning, 14:27, 1994.

    Google Scholar 

  8. Johnson T. and Dasu, T. “Comparing massive high-dimensional data sets,” KDD-98.

    Google Scholar 

  9. Lane, T. and Brodley, C. “Approaches to online learning and concept drift for user identification in computer security.” KDD-98, 1998.

    Google Scholar 

  10. Liu, B., Hsu, W., “Post analysis of learnt rules.” AAAI-96.

    Google Scholar 

  11. Liu, B., Hsu, W., and Chen, S. “Using general impressions to analyze discovered classification rules.” KDD-97, 1997, pp. 31–36.

    Google Scholar 

  12. Merz, C.J, and Murphy, P. UCI repository of machine learning databases http://www.cs.uci.edu/~mlearn/MLRepository.html, 1996.

  13. Moore, D.S. “Tests for chi-squared type.” In: R. B. D’Agostino and M. A. Stephens (eds), Googness-of-Fit Techniques, Marcel Dekker, New York, 1996, pp. 63–95.

    Google Scholar 

  14. Nakhaeizadeh, G., Taylor, C. and Lanquillon, C. “Evaluating usefulness of dynamic classification”, KDD-98, 1998.

    Google Scholar 

  15. Quinlan, R. C4.5: program for machine learning. Morgan Kaufmann, 1992.

    Google Scholar 

  16. Silberschatz, A., and Tuzhilin, A. “What makes patterns interesting in knowledge discovery systems.” IEEE Trans. on Know. and Data Eng. 8(6), 1996, pp. 970–974.

    Article  Google Scholar 

  17. Widmer, G. “Learning in the presence of concept drift and hidden contexts.” Machine learning, 23 69–101, 1996.

    MathSciNet  Google Scholar 

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© 2000 Springer-Verlag Berlin Heidelberg

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Liu, B., Hsu, W., Han, H.S., Xia, Y. (2000). Mining Changes for Real-Life Applications. In: Kambayashi, Y., Mohania, M., Tjoa, A.M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2000. Lecture Notes in Computer Science, vol 1874. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44466-1_34

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  • DOI: https://doi.org/10.1007/3-540-44466-1_34

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  • Print ISBN: 978-3-540-67980-6

  • Online ISBN: 978-3-540-44466-4

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