Abstract
Modeling probabilistic data is one of important issues in databases due to the fact that data is often uncertainty in real-world applications. So, it is necessary to identify potentially useful patterns in probabilistic databases. Because probabilistic data in 1NF relations is redundant, previous mining techniques don’t work well on probabilistic databases. For this reason, this paper proposes a new model for mining probabilistic databases. A partition is thus developed for preprocessing probabilistic data in a probabilistic databases. We evaluated the proposed technique, and the experimental results demonstrate that our approach is effective and efficient.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
R. Agrawal, T. Imielinski, and A. Swami, Mining association rules between sets of items in large databases. In: Proceedings of ACM SIGMOD, 1993:207–216.
R. Cromp and W. Campbell: Data Mining of Multi-dimensional Remotely Sensed Images. In: Proceedings of CIKM. 1993: 471–480.
D. Dey and S. Sarkar, A probabilistic relational model and algebra, ACM Trans. on Database Systems, Vol. 21 3(1996):339–369.
J. Han, Y. Cai and N. Cercone, Data-driven discovery of quantitative rules in relational databases. IEEE TKDE, Vol. 5, 1(1993):29–40.
K. Han, J. Koperski, and N. Stefanovic, GeoMiner: A system prototype for spatial data mining. SIGMOD Record Vol. 26, 2(1997): 553–556.
J. Pearl, Probabilistic reasoning in intelligent systems: Networks of plausible inference. Morgan Kaufmann Publishers, 1988.
R. Srikant and R. Agrawal, Mining quantitative association rules in large relational tables. In: Proceedings of ACM SIGMOD, 1996: 1–12.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, S., Zhang, C. (2001). Pattern Discovery in Probabilistic Databases. In: Stumptner, M., Corbett, D., Brooks, M. (eds) AI 2001: Advances in Artificial Intelligence. AI 2001. Lecture Notes in Computer Science(), vol 2256. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45656-2_53
Download citation
DOI: https://doi.org/10.1007/3-540-45656-2_53
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42960-9
Online ISBN: 978-3-540-45656-8
eBook Packages: Springer Book Archive