Abstract
We introduce the measures share, coincidence and dominance as alternatives to the standard itemset methodology measure of support. An itemset is a group of items bought together in a transaction. The support of an itemset is the ratio of transactions containing the itemset to the total number of transactions. The share of an itemset is the ratio of the count of items purchased together to the total count of items in all transactions. The coincidence of an itemset is the ratio of the count of items in that itemset to the total of those same items in the database. The dominance of an item in an itemset specifies the extent to which that item dominates the total of all items in the itemset. Share based measures have the advantage over support of reflecting accurately how many units are being moved by a business. The share measure can be extended to quantify the financial impact of an itemset on the business.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules,” in Proceedings of the 20th VLDB Conference, Santiago, Chile, 1994, 487–499.
R. Agrawal, H. Mannila, R. Srikant, H. Toivonen and A.I. Verkamo, “Fast Discovery of Association Rules,” in U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth and R. Uthurusamy, eds., Advances in Knowledge Discovery and Data Mining, AAAI Press/MIT Press, Menlo Park, CA, 1996, 307–328.
J. Han and Y. Fu, “Discovery of Multiple-Level Association Rules from Large Databases,” in Proceedings of the 21st VLDB Conference, Zurich, Switzerland, 1995.
M. Houtsma and A. Swami, “Set-Oriented Mining for Association Rules in Relational Databases,” in Proceedings of IEEE International Conference on Data Engineering, March, 1995, 25–33.
M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen and A. I. Verkamo, “Finding Interesting Rules from Large Sets of Discovered Association Rules,” in Proceedings of the Third International Conference on Information and Knowledge Management, Gaithersburg, Maryland, Nov., 1994.
B. Masand and G. Piatetsky-Shapiro, “A Comparison of Approaches for Maximizing Business Payoff of Prediction Models,” in Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Portland, Oregon, August, 1996, 195–201.
J. S. Park, M. S. Chen and P. S. Yu, “An Effective Hash Based Algorithm for Mining Association Rules,” in SIGMOD Record, 24:2, 1995, 175–186.
R. Srikant and R. Agrawal, “Mining Generalized Association Rules,” in Proceedings of the 21st VLDB Conference, Zurich, Switzerland, 1995.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Carter, C.L., Hamilton, H.J., Cercone, N. (1997). Share based measures for itemsets. In: Komorowski, J., Zytkow, J. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 1997. Lecture Notes in Computer Science, vol 1263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63223-9_102
Download citation
DOI: https://doi.org/10.1007/3-540-63223-9_102
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63223-8
Online ISBN: 978-3-540-69236-2
eBook Packages: Springer Book Archive