Mining the change of customer behavior in an internet shopping mall

Abstract Understanding and adapting to changes of customer behavior is an important aspect for a internet-based company to survive in a continuously changing environment. The aim of this paper is to develop a methodology which detects changes of customer behavior automatically from customer profiles and sales data at different time snapshots. For this purpose, we first define the three types of changes as emerging pattern, unexpected change and the added/perished rule, then, we develop similarity and difference measures for rule matching to detect all types of change. Finally, the degree of change is evaluated to detect significantly changed rules. Our proposed methodology can evaluate the degree of changes as well as detect all kinds of change automatically from different time snapshot data. A case study on an internet shopping mall for evaluation of this methodology is also provided.

[1]  Jinyan Li,et al.  Eecient Mining of Emerging Patterns: Discovering Trends and Diierences , 1999 .

[2]  Philip S. Yu,et al.  Online algorithms for finding profile association rules , 1998, CIKM '98.

[3]  Jiawei Han,et al.  Maintenance of discovered association rules in large databases: an incremental updating technique , 1996, Proceedings of the Twelfth International Conference on Data Engineering.

[4]  Wynne Hsu,et al.  Post-Analysis of Learned Rules , 1996, AAAI/IAAI, Vol. 1.

[5]  Yonatan Aumann,et al.  Efficient Algorithms for Discovering Frequent Sets in Incremental Databases , 1997, DMKD.

[6]  Carsten Lanquillon,et al.  Evaluating Usefulness for Dynamic Classification , 1998, KDD.

[7]  Balaji Padmanabhan,et al.  Unexpectedness as a Measure of Interestingness in Knowledge Discovery , 1999, Decis. Support Syst..

[8]  Abraham Silberschatz,et al.  What Makes Patterns Interesting in Knowledge Discovery Systems , 1996, IEEE Trans. Knowl. Data Eng..

[9]  Yishay Mansour,et al.  Learning Under Persistent Drift , 1997, EuroCOLT.

[10]  Heikki Mannila,et al.  Rule Discovery from Time Series , 1998, KDD.

[11]  Dimitrios Gunopulos,et al.  Finding Similar Time Series , 1997, PKDD.

[12]  Wynne Hsu,et al.  Mining interesting knowledge using DM-II , 1999, KDD '99.

[13]  Wynne Hsu,et al.  Using General Impressions to Analyze Discovered Classification Rules , 1997, KDD.

[14]  Hongjun Lu,et al.  Exception Rule Mining with a Relative Interestingness Measure , 2000, PAKDD.

[15]  Wynne Hsu,et al.  Mining Changes for Real-Life Applications , 2000, DaWaK.

[16]  David Wai-Lok Cheung,et al.  Maintenance of Discovered Knowledge: A Case in Multi-Level Association Rules , 1996, KDD.

[17]  Tomasz Imielinski,et al.  Database Mining: A Performance Perspective , 1993, IEEE Trans. Knowl. Data Eng..

[18]  Einoshin Suzuki,et al.  Autonomous Discovery of Reliable Exception Rules , 1997, KDD.

[19]  Ramakrishnan Srikant,et al.  Mining Association Rules with Item Constraints , 1997, KDD.

[20]  Johannes Gehrke,et al.  A framework for measuring changes in data characteristics , 1999, PODS '99.

[21]  Stephen D. Bay,et al.  Detecting change in categorical data: mining contrast sets , 1999, KDD '99.

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

[23]  Jiawei Han,et al.  Efficient mining of partial periodic patterns in time series database , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).

[24]  Sanjay Ranka,et al.  An Efficient Algorithm for the Incremental Updation of Association Rules in Large Databases , 1997, KDD.

[25]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

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

[27]  Carsten Lanquillon Information Filtering in Changing Domains , 1999, IJCAI 1999.