Skip to main content
SpringerLink
Log in

An Agent Model for Incremental Rough Set-Based Rule Induction in Customer Relationship Management

  • Conference paper
Hybrid Artificial Intelligent Systems (HAIS 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7208))

Included in the following conference series:

  • 1798 Accesses

Abstract

Compared to other methods, rough set (RS) has the advantage of combining both qualitative and quantitative information in decision analysis, which is extremely important for customer relationship management (CRM).

In this paper, we introduce an application of a multi-agent embedded incremental rough set-based rule induction to CRM, namely Incremental Rough Set-based Rule Induction Agent (IRSRIA). The rule induction is based on creating agents within the main modeling processes. This method is suitable for qualitative information and also takes into account user preferences. Furthermore, we designed an incremental architecture for addressing dynamic database problems of rough set-based rule induction, making it unnecessary to re-compute the whole dataset when the database is updated. As a result, huge degrees of computation time and memory space are saved when executing IRSRIA. Finally, we apply our method to a case study of a cell phone purchase. The results show the practical viability and efficiency of this method, and thus this paper forms the basis for solving many other similar problems that occur in the service industry.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Handen, L.: Putting CRM to work: The rise of the relationship. In: Brown, S.A. (ed.) Customer Relationship Management: A Strategic Imperative in the World of e-Business, pp. 7–18. Wiley, Toronto (2000)

    Google Scholar 

  2. Tseng, T.-L.B., Huang, C.-C., Ho, J.C.: Autonomous Decision Making in Customer Relationship Management: A Data Mining Approach. In: Proceeding of the Industrial Engineering Research 2008 Conference, Vancouver, British Columbia, Canada (2008)

    Google Scholar 

  3. Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  4. Kusiak, A.: Feature transformation methods in data mining. IEEE Transactions on Electronics Packaging Manufacturing 24, 214–221 (2001)

    Article  Google Scholar 

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

    Article  Google Scholar 

  6. Crespo, F., Weber, R.: A methodology for dynamic data mining based on fuzzy clustering. Fuzzy Sets and Systems 150, 267–284 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  7. Bakar, A.A., Othman, Z.A., Hamdan, A.R., Yusof, R., Ismail, R.: An agent model for rough classifiers. Applied Soft Computing 11, 2239–2245 (2011)

    Article  Google Scholar 

  8. Wang, X., Yang, J., Jensen, R., Liu, X.: Rough set feature selection and rule induction for prediction of malignancy degree in brain glioma. Computer Methods and Programs in Biomedicine 83, 147–156 (2006)

    Article  Google Scholar 

  9. Chengmin, S., Dayou, L., Chunxiao, F., Shuyang, S.: Algorithm Studies of Rules Generation in CORS. In: 5th IEEE International Conference on Cognitive Informatics, ICCI 2006, pp. 572–577 (2006)

    Google Scholar 

  10. Pattaraintakorn, P., Cercone, N.: Integrating rough set theory and medical applications. Applied Mathematics Letters 21, 400–403 (2008)

    Article  MathSciNet  Google Scholar 

  11. Tseng, T.-L.B., Huang, C.-C., Jiang, F., Ho, J.C.: Applying a hybrid data mining approach to prediction problems: A case of preferred suppliers prediction. International Journal of Production Research 44, 2935–2954 (2006)

    Article  MATH  Google Scholar 

  12. Billtseng, T., Huang, C.: Rough set-based approach to feature selection in customer relationship management. Omega 35, 365–383 (2007)

    Article  Google Scholar 

  13. Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann Publishers (2006)

    Google Scholar 

  14. Agrawal, R.K., Bala, R.: Incremental Bayesian classification for multivariate normal distribution data. Pattern Recognition Letters 29, 1873–1876 (2008)

    Article  Google Scholar 

  15. Awad, M., Motai, Y.: Dynamic classification for video stream using support vector machine. Applied Soft Computing 8, 1314–1325 (2008)

    Article  Google Scholar 

  16. Liu, Y., Xu, C., Pan, Y.: An incremental rule extracting algorithm based on Pawlak reduction. In: 2004 IEEE International Conference on Systems, Man and Cybernetics, vol. 5966, pp. 5964–5968 (2004)

    Google Scholar 

  17. Corchado, E., Graña, M., Woźniak, M.: Editorial: New trends and applications on hybrid artificial intelligence systems. Neurocomputing 75, 61–63 (2012)

    Article  Google Scholar 

  18. Corchado, E., Abraham, A., de Carvalho, A.: Editorial: Hybrid intelligent algorithms and applications. Inf. Sci. 180, 2633–2634 (2010)

    Article  Google Scholar 

  19. Pedrycz, W., Aliev, R.A.: Logic-oriented neural networks for fuzzy neurocomputing. Neurocomputing 73, 10–23 (2009)

    Article  Google Scholar 

  20. Liang, W.-Y., Huang, C.-C.: Agent-based demand forecast in multi-echelon supply chain. Decision Support Systems 42, 390–407 (2006)

    Article  Google Scholar 

  21. Luo, P., Lü, K., Huang, R., He, Q., Shi, Z.: A heterogeneous computing system for data mining workflows in multi-agent environments. Expert Systems 23, 258–272 (2006)

    Article  Google Scholar 

  22. Zhong, N., Dong, J.-Z., Ohsuga, S., Lin, T.Y.: An incremental, probabilistic rough set approach to rule discovery. In: IEEE International Conference on Fuzzy Systems, Anchorage, AK, USA, pp. 933–938 (1998)

    Google Scholar 

  23. Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Boston (1991)

    MATH  Google Scholar 

  24. Jiang, Y., Du, B.: A efficiency complete algorithm for attribute reduction. In: 2nd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2009, pp. 377–379 (2009)

    Google Scholar 

  25. Wang, C.-Y., Tseng, S.-S., Hong, T.-P.: Flexible online association rule mining based on multidimensional pattern relations. Information Sciences 176, 1752–1780 (2006)

    Article  MATH  Google Scholar 

  26. Bazan, J.G., Szczuka, M.S.: RSES and RSESlib - A Collection of Tools for Rough Set Computations. In: Ziarko, W.P., Yao, Y. (eds.) RSCTC 2000. LNCS (LNAI), vol. 2005, pp. 106–113. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Management, National Taiwan University, Taiwan

    Yu-Neng Fan & Ching-Chin Chern

Authors
  1. Yu-Neng Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ching-Chin Chern
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Universidad de Salamanca, Plaza de la Merced S/N, 37008, Salamanca, Spain

    Emilio Corchado

  2. VÅ B-TU Ostrava 17, Listopadu 15, 70833, Ostrava, Czech Republic

    Václav Snášel

  3. Machine Intelligence Research Labs(MIR Labs), Scientific Network for Innovation and Research Excellence, P.O. Box 2259, 98071, Auburn, Washington, USA

    Ajith Abraham

  4. Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Michał Woźniak

  5. University of the Basque Country, Pº Manuel Lardizabal 1, 20018, San Sebastian, Spain

    Manuel Graña

  6. Yonsei University, 134 Shinchon-dong, Sudaemoon-ku, 120-749, Seoul, Korea

    Sung-Bae Cho

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Fan, YN., Chern, CC. (2012). An Agent Model for Incremental Rough Set-Based Rule Induction in Customer Relationship Management. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, SB. (eds) Hybrid Artificial Intelligent Systems. HAIS 2012. Lecture Notes in Computer Science(), vol 7208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28942-2_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-28942-2_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28941-5

  • Online ISBN: 978-3-642-28942-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation