Abstract
Approaches to data mining proposed so far are mainly symbolic decision trees and numerical feedforward neural networks methods. While decision trees give, in many cases, lower accuracy compared to feedforward neural networks, the latter show black-box behaviour, long training times, and difficulty to incorporate available knowledge. We propose to use an incrementally-generated recurrent fuzzy neural network which has the following advantages over feedforward neural network approach: ability to incorporate existing domain knowledge as well as to establish relationships from scratch, and shorter training time. The recurrent structure of the proposed method is able to account for temporal data changes in contrast to both both feedforward neural network and decision tree approaches. It can be viewed as a gray box which incorporates best features of both symbolic and numerical methods. The effectiveness of the proposed approach is demonstrated by experimental results on a set of standard data mining problems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Agrawal R., Imielinski T., and Swami A.: Database Mining: A Performance Perspective. IEEE Trans. Knowledge and Data Engineering 5 (1993) 914–925
Breiman L., Friedman J. H., Olshen R. A., and Stone C. J.: Classification and Regression Trees: Wansworth International (1984)
Cercone N. and Tsuchiya M.: Special Issue on Learning and Discovery in Knowledge-based Databases. IEEE Trans. Knowledge and Data Engineering 5 (1993)
Dubois D. and Prade H.: A Unifying View of Comparison Indices in a Fuzzy Set Theoretic Framework. in Yager R. R. (ed.) Fuzzy Sets and Possibility Theory: Recent Developments: Pergamon NY (1982)
Frawley W. J., Piatetsky-Shapiro G., and Matheus C. J.: Knowledge Discovery in Databases: An Overview. In Piatetsky-Shapiro G. and Frawley W. J. (eds.): Knowledge discovery in databases: AAAI Press/MIT Press (1991) 1–27
Gallant S. I.: Connectionist Expert Systems. Communications of the ACM 32 (1988) 153–168
Kerber R.: Learning Classification Rules from Examples. Proc. 1991 AAAI Workshop on Knowledge Discovery in Databases: AAAI (1991)
Khan E. and Unal F.: Recurrent Fuzzy Logic Using Neural Networks. Proc. 1994 IEEE Nagoya World Wisepersons Workshop (1994) 48–55
Lu H., Setiono R., and Liu H.: Effective Data Mining Using Neural Networks. IEEE Trans. on Knowledge and Data Engineering 8 (1996) 957–961
Piatetsky-Shapiro G.: Special Issue on Knowledge Discovery in Databases — from Research to Applications. Int. J. of Intelligent Systems 5 (1995)
Quinlan J. R.: Induction of Decision Trees. Machine Learning 1 (1986) 81–106
Quinlan J. R.: C4.5:Programs for Machine Learning Morgan Kaufmann: San Mateo CA (1993)
Quinlan J. R.: Comparing Connectionist and Symbolic Learning Methods. In S. Hanson, G. Drastall, and R. Rivest (eds.): Computational Learning Theory and Natural Learning Systems: MIT Press 1 (1994) 445–456
Shavlik J. W., Mooney R. J., and Towell G. G.: Symbolic and Neural Learning Algorithms: An Experimental Comparison. Machine Learning 6 (1991) 111–143
Towell G. G. and Shavlik J. W.: Extracting Refined Rules From Knowledge-based Neural Networks. Machine Learning 13 (1993) 71–101
Wang X. Z., Chen B. H., Yang S. H., McGreavy C., Lu M. L.: Fuzzy Rule Generation From Data for Process Operational Decision Support. Computer and Chemical Engineering 21 (1997) 661–666
Wu X.: Knowledge Acquisition from Databases. Ablex Publishing: Norwood NJ (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Frayman, Y., Wang, L. (1998). Data mining using dynamically constructed recurrent fuzzy neural networks. In: Wu, X., Kotagiri, R., Korb, K.B. (eds) Research and Development in Knowledge Discovery and Data Mining. PAKDD 1998. Lecture Notes in Computer Science, vol 1394. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64383-4_11
Download citation
DOI: https://doi.org/10.1007/3-540-64383-4_11
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64383-8
Online ISBN: 978-3-540-69768-8
eBook Packages: Springer Book Archive