ACM SIGMOD Anthology VLDB dblp.uni-trier.de

NeuroRule: A Connectionist Approach to Data Mining.

Hongjun Lu, Rudy Setiono, Huan Liu: NeuroRule: A Connectionist Approach to Data Mining. VLDB 1995: 478-489
@inproceedings{DBLP:conf/vldb/LuSL95,
  author    = {Hongjun Lu and
               Rudy Setiono and
               Huan Liu},
  editor    = {Umeshwar Dayal and
               Peter M. D. Gray and
               Shojiro Nishio},
  title     = {NeuroRule: A Connectionist Approach to Data Mining},
  booktitle = {VLDB'95, Proceedings of 21th International Conference on Very
               Large Data Bases, September 11-15, 1995, Zurich, Switzerland},
  publisher = {Morgan Kaufmann},
  year      = {1995},
  isbn      = {1-55860-379-4},
  pages     = {478-489},
  ee        = {db/conf/vldb/LuSL95.html},
  crossref  = {DBLP:conf/vldb/95},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX

Abstract

Classification, which involves finding rules that partition a given data set into disjoint groups, is one class of data mining problems. Approaches proposed so far for mining classification rules for large databases are mainly decision tree based symbolic learning methods. The connectionist approach based on neural networks has been thought not well suited for data mining. One of the major reasons cited is that knowledge generated by neural networks is not explicitly represented in the form of rules suitable for verification or interpretation by humans. This paper examines this issue. With our newly developed algorithms, rules which are similar to, or more concise than those generated by the symbolic methods can be extracted from the neuralnetworks. The data mining process using neural networks with the emphasis on rule extraction is described. Experimental results and comparison with previously published works are presented.

Copyright © 1995 by the VLDB Endowment. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by the permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment.

Online Paper

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Printed Edition

Umeshwar Dayal, Peter M. D. Gray, Shojiro Nishio (Eds.): VLDB'95, Proceedings of 21th International Conference on Very Large Data Bases, September 11-15, 1995, Zurich, Switzerland. Morgan Kaufmann 1995, ISBN 1-55860-379-4
Contents BibTeX

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Referenced by

  1. David Wai-Lok Cheung, Sau Dan Lee, Ben Kao: A General Incremental Technique for Maintaining Discovered Association Rules. DASFAA 1997: 185-194
  2. Hongjun Lu, Rudy Setiono, Huan Liu: Effective Data Mining Using Neural Networks. IEEE Trans. Knowl. Data Eng. 8(6): 957-961(1996)
  3. Ming-Syan Chen, Jiawei Han, Philip S. Yu: Data Mining: An Overview from a Database Perspective. IEEE Trans. Knowl. Data Eng. 8(6): 866-883(1996)
  4. Hannu Toivonen: Sampling Large Databases for Association Rules. VLDB 1996: 134-145
BibTeX
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