Advertisement

An Interactive Approach for the Post-processing in a KDD Process

Conference paper
  • 2.4k Downloads
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 438)

Abstract

Association rule mining is a technique widely used in the field of data mining, which consists in discovering relationships and/or correlations between the attributes of a database. However, the method brings known problems among which the fact that a large number of association rules may be extracted, not all of them being relevant or interesting for the domain expert. In that context, we propose a practical, interactive and helpful guided approach to visualize, evaluate and compare the extracted rules following a step by step methodology, taking into account the interaction between the industrial domain expert and the data mining expert.

Keywords

Knowledge Discovery from Databases Association Rules Mining Post-processing phase Interactivity Decision Support System 

References

  1. 1.
    Giudici, P.: Applied data mining: Statistical methods for business and industry. Wiley (2003)Google Scholar
  2. 2.
    Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R.: Advances in knowl-edge discovery and data mining. MIT Press (1996)Google Scholar
  3. 3.
    Harding, J.A., Shahbaz, M., Shahbaz, S., Kusiak, A.: Data mining in manufacturing: A review. Journal of Manufacturing Science and Engineering - Transactions of the ASME 128, 969–976 (2006)CrossRefGoogle Scholar
  4. 4.
    Köksal, G., Batmaz, I., Testik, M.C.: A review of data mining applications for quality improvement in manufacturing industry. Expert Systems with Applications 38(10), 13448–13467 (2011)CrossRefGoogle Scholar
  5. 5.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB 1994, 1215th edn., pp. 487–499. Morgan Kaufmann Publishers Inc (1994)Google Scholar
  6. 6.
    Choudhary, A.K., Harding, J.A., Tiwari, M.K.: Data mining in manufacturing: a review based on the kind of knowledge. Journal of Intelligent Manufacturing 20(5), 501–521 (2009)CrossRefGoogle Scholar
  7. 7.
    Marinica, C.: Association Rule Interactive Post-processing using Rule Schemas and Ontolo-gies-ARIPSO. PhD thesis, Ecole polytechnique de l’Université de Nantes (2010)Google Scholar
  8. 8.
    Ben Ayed, M., Ltifi, H., Kolski, C., Alimi, A.M.: A user-centered approach for the de-sign and implementation of KDD-based DSS: A case study in the healthcare domain. Decision Support Systems 50(1), 64–78 (2010)CrossRefGoogle Scholar
  9. 9.
    Wang, H., Wang, S.: A knowledge management approach to data mining process for business intelligence. Industrial Management & Data Systems 108(5), 622–634 (2008)CrossRefGoogle Scholar
  10. 10.
    Geng, L., Hamilton, H.J.: Interestingness measures for data mining: A survey. ACM Computing Surveys 38(3), Article 9 (2006)Google Scholar
  11. 11.
    Baesens, B., Viaene, S., Vanthienen, J.: Post-processing of association rules. DTEW Research Report 0020, pp. 1–18 (2000)Google Scholar
  12. 12.
    Liu, B., Hsu, W., Wang, K., Chen, S.: Visually Aided Exploration of Interesting Association Rules. In: Zhong, N., Zhou, L. (eds.) PAKDD 1999. LNCS (LNAI), vol. 1574, pp. 380–389. Springer, Heidelberg (1999)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2014

Authors and Affiliations

  1. 1.Laboratoire Génie de ProductionINP-ENIT - Université de ToulouseTarbes CedexFrance

Personalised recommendations