Utilizing the Effect of Market Basket Size for Improving the Practicality of Association Rule Measures

ABSTRACT Association rule mining techniques enable us to acquire knowledge concerning sales patterns among individual items from voluminous transactional data. Certainly, one of the major purposes of association rule mining is utilizing the acquired knowledge to provide marketing strategies such as catalogue design, cross-selling and shop allocation. However, this requires too much time and high cost to only extract the actionable and profitable knowledge from tremendous numbers of discovered patterns. In currently available literature, a number of interest measures have been devised to accelerate and systematize the process of pattern evaluation. Unfortunately, most of such measures, including support and confidence, are prone to yielding impractical results because they are calculated only from the sales frequencies of items. For instance, traditional measures cannot differentiate between the purchases in a small basket and those in a large shopping cart. Therefore, some adjustment should be made to the size of market baskets because there is a strong possibility that mutually irrelevant items could appear together in a large shopping cart. Contrary to the previous approaches, we attempted to consider market basket’s size in calculating interest measures. Because the devised measure assigns different weights to individual purchases according to their basket sizes, we expect that the measure can minimize distortion of results caused by accidental patterns. Additionally, we performed intensive computer simulations under various environments, and we performed real case analyses to analyze the correctness and consistency of the devised measure.Keywords:Association Rule Mining, Data Mining, Market Basket Analysis, Interest Measures

[1]  Wen-Yang Lin,et al.  Automated support specification for efficient mining of interesting association rules , 2006, J. Inf. Sci..

[2]  한경록.  CRM과 SCM의 전략적 통합을 위한 데이터 마이닝의 활용 , 2008 .

[3]  Gab-Soo Han,et al.  Approximation of Frequent Itemsets with Maximum Size by One-scan for Association Rule Mining Application , 2008 .

[4]  David L. Olson,et al.  Introduction to Business Data Mining , 2005 .

[5]  Ramakrishnan Srikant,et al.  The Quest Data Mining System , 1996, KDD.

[6]  Patrick Meyer,et al.  On selecting interestingness measures for association rules: User oriented description and multiple criteria decision aid , 2008, Eur. J. Oper. Res..

[7]  Keun Ho Ryu,et al.  Frequent Patterns Mining using only one-time Database Scan , 2008 .

[8]  Howard J. Hamilton,et al.  Interestingness measures for data mining: A survey , 2006, CSUR.

[9]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[10]  Patrick Meyer,et al.  Association Rule Interestingness Measures: Experimental and Theoretical Studies , 2007, Quality Measures in Data Mining.

[11]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[12]  Colin Cooper,et al.  Realistic Synthetic Data for Testing Association Rule Mining Algorithms for Market Basket Databases , 2007, PKDD.

[13]  Howard J. Hamilton,et al.  Extracting Share Frequent Itemsets with Infrequent Subsets , 2003, Data Mining and Knowledge Discovery.

[14]  Jaideep Srivastava,et al.  Selecting the right interestingness measure for association patterns , 2002, KDD.

[15]  Ke Wang,et al.  Pushing Support Constraints Into Association Rules Mining , 2003, IEEE Trans. Knowl. Data Eng..

[16]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.