Abstract
The discovery of association rules is a very efficient data mining technique that is especially suitable for large amounts of categorical data. This paper shows how the discovery of association rules can be of benefit for numeric data as well. Based on a review of previous approaches we introduce Q2, a faster algorithm for the discovery of multi-dimensional association rules over ordinal data. We experimentally compare the new algorithm with the previous approach, obtaining performance improvements of more than an order of magnitude on supermarket data. In addition, a new absolute measure for the interestingness of quantitative association rules is introduced. It is based on the view that quantitative association rules have to be interpreted with respect to their Boolean generalizations. This measure has two major benefits compared to the previously used relative interestingness measure; first, it speeds up rule extraction and evaluation and second, it is easier to interpret for a user. Finally we introduce a rule browser which supports the exploration of ordinal data with quantitative association rules.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
R. Agrawal, T. Imielinski, A. Swami. Mining association rules between sets of items in large databases. In: Proc. 1993 ACM SIGMOD Conference, Washington DC, USA, May 1993.
K. Ali, S. Manganaris. Partial classification using association rules. In: Proc. 3rd International Conference on KDD and Data Mining, Newport Beach California, August 1997.
R. Agrawal, R. Srikant. Fast algorithms for mining association rules. In: Proc. 20th VLDB Conference, Santiago, Chile, 1994.
M. Bell. A Data Mining FAQ, 1997, http://www.qwhy.com/dmfaq.htm, 23.07.97.
K. Backhaus, B. Erichson, W. Plinke, R. Weiber. Multivariate Analysemethoden, Springer: Berlin, 1996.
S. Brin, R. Motwani, C. Silverstein. Beyond market baskets: Generalizing association rules to correlations. In: 1997 ACM SIGMOD Conference on Management of Data, 1997, pp. 265-276.
T. Fukuda, Y. Morimoto, S. Morishita, T. Tokuyama. Mining optimized association rules for numeric attributes. In: Proc. 15th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, Montreal, Canada, 1996.
U. Fayyad, G. Piatetsky-Shapiro, P. Smyth. From data mining to knowledge discovery. An overview. In; U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth (Hrsg.), Advances in Knowledge Discovery and Data Mining, AAAI Press: Menlo Park, 1996, S. 1–36.
F. Gebhardt. Interessantheit als Kriterium für die Bewertung von Ergebnissen, Informatik Forschung und Entwicklung 9, 9–21, 1994.
J. Hipp, A.s Myka, R. Wirth, U. Güntzer. A new algorithm for faster mining of generalized association rules. In: Proc. 2nd European Symposium on Principles of Data Mining and Knowledge Discovery (PKDD ’98), Nantes, France, 1998.
D. Klunzinger. Darstellung von Assoziationsregeln im Kontext Warenkorbanalyse, Diplomarbeit. University of Stuttgart, 1997.
M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, A. I. Verkamo. Finding interesting rules from large sets of discovered association rules. In: Proc. Third International Conference on Information and Knowledge Management (CIKM’94), Gaithersburg, Maryland, 1994.
J. A. Major, J. J. Mangano. Selecting among rules induced from a hurricane database. In: Knowledge Discovery in Databases Workshop, 1993.
R. J. Miller, Y. Yang. Association rules over interval data. In: ACM SIGMOD 1997, Tucson, Arizona, May 1997.
G. Piatetsky-Shapiro, C. J. Matheus. The interestingness of deviations. In: AAAI-94 Workshop on Knowledge Discovery in Databases, 1994, pp. 25-36.
J. R. Quinlan. C4.5: Programs for Machine Learning, Morgan Kaufmann: San Mateo, 1993.
R. Srikant, R. Agrawal. Mining generalized association rules. In: Proc. 21st Conference on Very Large Databases, Zurich, Switzerland, 1995.
R. Srikant, R. Agrawal. Mining quantitative association rules in large relational tables. In: Proc. ACM SIGMOD Conference on Management of Data, Montreal, Canada, June 1996.
A. Silberschatz, A. Tuzhilin. What makes patterns interesting in knowledge discovery in databases, IEEE Trans. Knowledge and Data Engineering, 8(6), 970–974, 1996.
H. Toivonen. Discovery of frequent patterns in large data collections, PhD thesis, University of Helsinki, Department of Computer Science, November 1996.
K. Yoda, T. Fukuda, Y. Morimoto, S. Morishita, T. Tokuyama. Computing optimized rectilinear regions for association rules. In: Proc. 3rd International Conference on KDD and Data Mining, Newport Beach California, August 1997.
M. J. Zaki, S. Parthasarathy, M. Ogihara, W. Li. New algorithms for fast discovery of association rules. In: Proc. 3rd International Conference on KDD and Data Mining, Newport Beach California, August 1997.
Z. Zhang, Y. Lu, B. Zhang. An effective partitioning-combining algorithm for discovering quantitative association rules. In: PAKDD97, 1997.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Büchter, O., Wirth, R. Exploration of Ordinal Data Using Association Rules. Knowledge and Information Systems 1, 393–414 (1999). https://doi.org/10.1007/BF03325107
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/BF03325107