Association Rules in Data Mining: An Application on a Clothing and Accessory Specialty Store

Mutlu Yüksel Avcilar, Emre Yakut

Abstract


Retailers provide important functions that increase the value of the products and services they sell to consumers. Retailers value creating functions are providing assortment of products and services: breaking bulk, holding inventory, and providing services. For a long time, retail store managers have been interested in learning about within and cross-category purchase behavior of their customers, since valuable insights for designing marketing and/or targeted cross-selling programs can be derived. Especially, parallel to the development of information processing and communication technologies, it has become possible to transfer customers shopping information into databases with the help of barcode technology. Data mining is the technique presenting significant and useful information using of lots of data. Association rule mining is realized by using market basket analysis to discover relationships among items purchased by customers in transaction databases. In this study, association rules were estimated by using market basket analysis and taking support, confidence and lift measures into consideration. In the process of analysis, by using of data belonging to the year of 2012 from a clothing and accessory specialty store operating in the province of Osmaniye, a set of data related to 42.390 sales transactions including 9.000 different product kinds in 35 different product categories (SKU) were used. Analyses were carried out with the help of SPSS Clementine packet program and hence 25.470 rules were determined.


Keywords


Specialty retailer store; Data mining; Association rules

Full Text:

PDF

References


Agrawal, R., Imielinski, T., & Swami, A. (1993, May). Mining association rules between sets of items in large databases. ACM SIGMOD Conference, Washington DC, USA.

Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. VLDB (Very Large Data Base Endowment), 20th VLDB Conference, Santiago, Chile.

Ahn, K. (2012). Effective product assignment based on association rule mining in retail. Expert Systems with Applications, 39, 551-556.

Aloysius, G., & Binu, D. (2013). An approach to products placement in supermarkets using prefixspan algorithm. Journal of King Saud University-Computer and Information Sciences, 25, 77-87.

Ay, D., & Çil, İ. (2008). Using association rules for Migros Turk in the development of layout plan. Journal of Industrial Engineering, 21(2), s.14-29.

Brijs, T., Swinnen, G., Vanhoof, K., & Wets, G. (2004). Building an association rules framework to improve product assortment decisions. Data Mining and Knowledge Discovery, 8, 7-23.

Brin, S., Motwani, R., Ullman, J. D., & Tsur, S. (1997). Dynamic item set counting and implication rules for market basket data. SİGMOD, Acm-sigmod international conference on management of data, New York, NY, USA.

Changchien, S. W., & Lu, T. (2001). Mining association rules procedure to support on-line recommendation by customers and products fragmentation. Expert Systems With Applications, 20, 325-335.

Chen, M., Han, J., & Yu, P. (1996). Data mining: An overview from a database perspective. IEEE Transactions on Knowledge and Data Engineering, 8(6), 866-883.

Chen, M., Chiu, A., & Chang, H. (2005). Mining changes in customer behavior in retail marketing. Expert Systems With Applications, 28, 773-781.

Chen, M., & Lin, C. (2007). A data mining approach to product assortment and shelf space allocation. Expert Systems With Applications, 32, 976-986.

Chen, Y., Huang, T. C., & Chang, S. (2008). A novel approach for discovering retail knowledge with price information from transaction databases. Expert Systems With Applications, 34, 2350-2359.

Demiriz, A., Ertek, G., Atan, T., & Kula, U. (2011). Re-mining item associations: Methodology and a case study in apparel retailing. Decision Support Systems, 52, 284-293.

Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3), 37-54.

Han, J., & Fu, Y. (1995). Discovery of multiple-level association rules from large databases. 21st VLDB (Very Large Data Base Endowment) Conference, Zurich, Switzerland.

Han, J., Kamber, M., & Pei, J. (2012). Data mining: Concepts and techniques (3rd ed.). San Francisco: Morgan Kaufmann Inc.

Kamakura, W. A. (2012). Sequential market basket analysis. Mark Lett, 23, 505-516.

Levy, M., & Weitz, B. A. (2004). Retailing management (5th ed.). Boston: Irwin McGraw-Hill Inc.

Linoff, G. S., & Berry, M. J. (2011). Data mining techniques: For marketing, sales and customer relationship management (3rd ed.). Indianapolis: Wiley Publishing Inc.

Liao, S., & Chen, Y. (2004). Mining customer knowledge for electronic catalog marketing. Expert Systems with Applications, 27, 521-532.

Liao, S., Chen, C., & Wu, C. (2008). Mining customer knowledge for product line and brand extension in retailing. Expert Systems with Applications, 34, 63-76.

Liu, B., Hsu, W., & Ma, Y. (1998). Integrating classification and association rule mining. Knowledge Discovery and Data Mining, 80-86.

Nafari, M., & Shahrabi, J. (2010). A temporal data mining approach for shelf-space allocation with consideration of product price. Expert Systems with Applications, 37, 66-72.

Romero, C., Luna, J. M., Romero, J. R., & Ventura, S. (2011). RM-Tool: A framework for discovering and evaluating association rules. Advances in Engineering Software, 42, .566-576.

Seng, J., & Chen, T. C. (2010). An analytic approach to select data mining for business decision. Expert Systems with Applications, 37, 42-57.

Sohn, S. Y., & Kim, Y. (2008). Searching customer patterns of mobile service using clustering and quantitative association rule. Expert Systems With Applications, 34, 70-77.

Srikant, R., & Agrawal, R. (19950. Mining generalized association rules. 21st VLDB (Very Large Data Base Endowment) Conference, Zurich, Switzerland.

Tan, P., Steinbach, M., & Kumar, V. (2006). Introduction to data mining. Boston: Pearson Education.

Tang, K., Chen, Y., & Hu, H. (2008). Context-based market basket analysis in a multiple-store environment. Decision Support Systems, 45, 150-163.

Tsai, P. S. M., & Chen, C. (2004). Mining interesting association rules from customer databases and transaction databases. Information Systems, 29, 685-696.

Zhou, L., & Yau, S. (2007). Efficient association rule mining among both frequent and infrequent items. Computers and Mathematics With Applications, 54, 737-749.




DOI: http://dx.doi.org/10.3968/%25x

Refbacks

  • There are currently no refbacks.


Copyright (c)



Reminder

  • How to do online submission to another Journal?
  • If you have already registered in Journal A, then how can you submit another article to Journal B? It takes two steps to make it happen:

1. Register yourself in Journal B as an Author

  • Find the journal you want to submit to in CATEGORIES, click on “VIEW JOURNAL”, “Online Submissions”, “GO TO LOGIN” and “Edit My Profile”. Check “Author” on the “Edit Profile” page, then “Save”.

2. Submission

Online Submission: http://cscanada.org/index.php/css/submission/wizard

  • Go to “User Home”, and click on “Author” under the name of Journal B. You may start a New Submission by clicking on “CLICK HERE”.
  • We only use four mailboxes as follows to deal with issues about paper acceptance, payment and submission of electronic versions of our journals to databases: caooc@hotmail.com; office@cscanada.net; ccc@cscanada.net; ccc@cscanada.org

 Articles published in Canadian Social Science are licensed under Creative Commons Attribution 4.0 (CC-BY).

 

Canadian Social Science Editorial Office

Address: 1020 Bouvier Street, Suite 400, Quebec City, Quebec, G2K 0K9, Canada.
Telephone: 1-514-558 6138 
Website: Http://www.cscanada.net; Http://www.cscanada.org 
E-mail:caooc@hotmail.com; office@cscanada.net

Copyright © Canadian Academy of Oriental and Occidental Culture