Abstract
Data mining has been defined as the non- trivial extraction of implicit, previously unknown and potentially useful information from data. Association mining is one of the important sub-fields in data mining, where rules that imply certain association relationships among a set of items in a transaction database are discovered.
The efforts of most researchers focus on discovering rules in the form of implications between itemsets, which are subsets of items that have adequate supports. Having itemsets as both antecedent and precedent parts was motivated by the original application pertaining to market baskets and they represent only the simplest form of predicates. This simplicity is also due in part to the lack of a theoretical framework that includes more expressive predicates.
The framework we develop derives from the observation that information retrieval and association mining are two complementary processes on the same data records or transactions. In information retrieval, given a query, we need to find the subset of records that matches the query. In contrast, in data mining, we need to find the queries (rules) having adequate number of records that support them.
In this paper we introduce the theory of association mining that is based on a model of retrieval known as the Boolean Retrieval Model. The potential implications of the proposed theory are presented.
This is a preview of subscription content, log in via an institution to check access.
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. Imilienski, and A. Swami, “Mining Association Rules between Sets of Items in Large Databases,” Proc. of the ACM SIGMOD Int’l Conf. On Management of data, May 1993
R. Agrawal, and R. Srikant, “Fast Algorithms for Mining Association Rules,” Proc. Of the 20th VLDB Conference, Santiago, Chile, 1994.
R. Agrawal, and P. Yu, “Parallel Mining of Association Rules,” IEEE Transactions on Knowledge and Data Engineering, Vol. 8, No, 6, Dec. 1996.
C. Agrawal, and P. Yu, “Mining Large Itemsets for Association Rules,” Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, 1997.
A. Bookstein and W. Cooper, “A General Mathematical Model for Information Retrieval Systems,” Library Quarterly, 46(2), 153–167.
S. Brin, R. Motwani, J. Ullman, and S. Tsur, “Dynamic Itemset Counting and Implication Rules for Market Basket Data,” SIGMOD Record (SCM Special Interset Group on Management of Data), 26,2, 1997.
S. Chaudhuri, “Data Mining and Database Systems: Where is the Intersection,”Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, 1997.
U. Fayyad, “Mining Databases: Towards Algorithms for Knowledge Discovery,”Data Engineering Bulletin 21(1): 39–48 (1998)
Fayyad: Editorial. Data Mining and Knowledge Discovery 2(1): 5–7 (1998)
U. Fayyad, “Data Mining and Knowledge Discovery in Databases: Implications for Scientific Databases,” SSDBM 1997: 2–11
H. Mannila, “Inductive databases and condensed representations for data mining,” Proc. International Logic Programming Symposium, 1997, pp. 21–30.
H. Mannila, “Methods and problems in data mining,” In Proceedings of the International Conference on Database Theory, Delphi, Greece, January 1997. Springer-Verlag.
H. Mannila, “Data mining: machine learning, statistics, and databases,” Eight International Conference on Scientific and Statistical Database Management, Stockholm. 1996.
H. Mannila, H. Toivonen, and A. Verkamo, “Efficient Algorithms for Discovering Association Rules,” AAAI Workshop on Knowledge Discovery in databases (KDD-94) , July 1994.
A. Netz, S. Chaudhuri, J. Bernhardt and U. Fayyad, “Integration of Data Mining with Database Technology,” VLDB 2000: 719–722
S. E. Robertson, “Theories and Models in Information Retrieval,” Journal of Documentation, 33, 126–149.
G. Salton, Automatic Text Processing, Reading, MA: Addison Wesley.
M. Zaki and M. Ogihara, “Theoretical foundations of association rules,” In 3rd ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, June 1998.
M. Zaki, S. Parthasarathy, M. Ogihara, and W. Li, “ New Algorithms for Fast Discovery of Association Rules,”Proc. Ofthe 3rd Int’l Conf. On Knowledge Discovery and data Mining (KDD-97), AAAI Press, 1997
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
Bollmann-Sdorra, P., Hafez, A.M., Raghavan, V.V. (2001). A Theoretical Framework for Association Mining based on the Boolean Retrieval Model. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2001. Lecture Notes in Computer Science, vol 2114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44801-2_3
Download citation
DOI: https://doi.org/10.1007/3-540-44801-2_3
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42553-3
Online ISBN: 978-3-540-44801-3
eBook Packages: Springer Book Archive