Distributed Architecture for Association Rule Mining

  • Marko Banek
  • Damir Jurić
  • Ivo Pejaković
  • Zoran Skočir
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4243)

Abstract

Organizations have adopted various data mining techniques to support their decision-making and business processes. However, the mining analysis is not performed and supervised by the final user, the management of the organization, since the knowledge of mathematical models as well as expert database administration skills is required. This paper describes a distributed architecture for association rule mining analysis in the retail area, designed to be used directly by the management of an organization and implemented as a Java web application. The rule discovery algorithm is executed at the database server that hosts the source data warehouse, while the only used client tool is a web browser. The user interactively initiates the rule discovery process through a simple user interface, which is used later to browse, sort and compare the discovered rules.

Keywords

Association Rule Fact Table Apriori Algorithm Rule Discovery Algorithm Execution 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules between Sets of Items in Large Databases. In: Proc. 1993 ACM SIGMOD, pp. 207–216. ACM Press, New York (1993)CrossRefGoogle Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Proc. VLDB Conf (VLDB 1994), pp. 487–499. Morgan Kaufmann, San Francisco (1994)Google Scholar
  3. 3.
    Berry, M.J.A., Linoff, G.S.: Data Mining Techniques for Marketing, Sales, and Customer Relationship Management, 2nd edn. Wiley Publishing Inc., Indianapolis (2004)Google Scholar
  4. 4.
    Brin, S., Motwani, R., Ullman, J.D., Tsur, S.: Dynamic Itemset Counting and Implication Rules for Market Basket Data. In: Proc. ACM SIGMOD Int. Conf. on Management of Data. SIGMOD Record, vol. 26(2), pp. 255–264. ACM Press, New York (1997)CrossRefGoogle Scholar
  5. 5.
    Kimball, R., Ross, M.: The Data Warehouse Toolkit, The Complete Guide to Dimensional Modeling, 2nd edn. John Wiley & Sons, New York (2002)Google Scholar
  6. 6.
    Nestorov, S., Jukic, N.: Ad-Hoc Association-Rule Mining within the Data Warehouse. In: Abstract Proc. Hawaii Int. Conf. on System Sciences (HICSS 2003), vol. 232. IEEE Computer Society Press, Los Alamitos (2003), http://people.cs.uchicago.edu/~evtimov/pubs/hicss03.pdf Google Scholar
  7. 7.
    Ma, Y., Liu, B., Wong, C.K.: Web for Data Mining: Organizing and Interpreting the Discovered Rules Using the Web. In: SIGKDD Explorations, vol. 2(1), pp. 16–238. ACM Press, New York (2000)Google Scholar
  8. 8.
    Zaky, M.J., Parthasarathy, S., Ogihara, M., Li, W.: New Algorithm for Fast Discovery of Association Rules. Technical Report No. 261. University of Rochester (1997), http://cs.aue.aau.dk/contribution/projects/datamining/papers/tr651.pdf
  9. 9.
    Hand, D., Manilla, H., Smyth, P.: Principles of Data Mining. MIT Press, Cambridge, London (2001)Google Scholar
  10. 10.
    Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proc. ACM-SIGMOD Int. Conf. on Management of Data, pp. 1–12. ACM Press, New York (2000)CrossRefGoogle Scholar
  11. 11.
    Pejaković, I., Sko\(\check{c}\)ir, Z., Medved, D.: Descriptive Data Mining Modeling in Telecom Systems. In: Proc. Int. Conf. on Software, Telecommunicatins and Computer Networks (SoftCom 2004), University of Split, Split, pp. 199–203 (2004)Google Scholar
  12. 12.
    Weka 3. Data Mining Software in Java. Version 3.4. University of Waikato (2003), http://www.cs.waikato.ac.nz/ml/weka/
  13. 13.
    Oracle Data Mining Concepts 10g Release 1 (10.1). Oracle Corporation (2003), http://oraclelon1.oracle.com/docs/pdf/B10698_01.pdf
  14. 14.
    Java 2 Platform, Enterprise Edition. Java Servlet Specification, version 2.4. Sun Microsystems (2003), http://java.sun.com/j2ee/15
  15. 15.
  16. 16.
    JavaServer Pages Specification, version 2.0. Sun Microsystems (2003), http://java.sun.com/products/jsp/reference/api/index.html
  17. 17.
    Apache Tomcat Servlet/JSP Container, version 5.5, The Apache Software Foundation (2005), http://tomcat.apache.org/
  18. 18.

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Marko Banek
    • 1
  • Damir Jurić
    • 1
  • Ivo Pejaković
    • 2
  • Zoran Skočir
    • 1
  1. 1.FERUniversity of ZagrebZagrebCroatia
  2. 2.MetronetZagrebCroatia

Personalised recommendations