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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Agrawal, R. and Psaila, G. “Active data mining” KDD-95, 1995.
Agrawal, R., Imielinski, T., Swami, A. “Mining association rules between sets of items in large databases” SIGMOD-1993, 1993, pp. 207–216.
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.
Dong, G. and Li, J. “Efficient mining of emerging patterns: discovering trends and differences.” KDD-99, 1999.
Freund, Y and Mansour, Y. “Learning under persistent drift” Computational learning theory: Third European conference, 1997.
Ganti, V., Gehrke, J., and Ramakrishnan, R. “A framework for measuring changes in data characteristics” POPS-99.
Helmbold, D. P. and Long, P. M. “Tracking drifting concepts by minimizing disagreements.” Machine Learning, 14:27, 1994.
Johnson T. and Dasu, T. “Comparing massive high-dimensional data sets,” KDD-98.
Lane, T. and Brodley, C. “Approaches to online learning and concept drift for user identification in computer security.” KDD-98, 1998.
Liu, B., Hsu, W., “Post analysis of learnt rules.” AAAI-96.
Liu, B., Hsu, W., and Chen, S. “Using general impressions to analyze discovered classification rules.” KDD-97, 1997, pp. 31–36.
Merz, C.J, and Murphy, P. UCI repository of machine learning databases http://www.cs.uci.edu/~mlearn/MLRepository.html, 1996.
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.
Nakhaeizadeh, G., Taylor, C. and Lanquillon, C. “Evaluating usefulness of dynamic classification”, KDD-98, 1998.
Quinlan, R. C4.5: program for machine learning. Morgan Kaufmann, 1992.
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.
Widmer, G. “Learning in the presence of concept drift and hidden contexts.” Machine learning, 23 69–101, 1996.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/3-540-44466-1_34
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67980-6
Online ISBN: 978-3-540-44466-4
eBook Packages: Springer Book Archive