A Brief Review of Automatic Rule Generation
##semicolon##
https://doi.org/10.26821/IJSHRE.9.6.2021.9618##semicolon##
Automatic Rule Generation##common.commaListSeparator## Partitionसार
Automatic rule generation can be regarded as rule
generation techniques, and it can be decomposed into
rule induction and rule-base optimization. As the
aspect of the size of the system, it just need to work
rule induction in a simple or a small system with a few
variables in the relative database without processing
the step of rule-base optimization, because the
performance of rule-base optimization is almost
achieved by the step of rule induction in a simple or a
small system. So far as the size of work space, the
family of shared partitions is suitable to work in a
small size work space with a good convergence, and
the family of clustering is appropriate to work in a
large size work space with a good convergence.
However, the family of shared partitions is easy to
make the problem of curse of dimensionality when
dealing with large systems.
##submission.citations##
Guillaume, S., (2001), Designing Fuzzy Inference
Systems from Data: An Interpretability-Oriented
Review, IEEE Transactions on Fuzzy Systems,
Vol. 9, No. 3, pp. 426-443.
Ishibuchi, H., Nozaki, K., Tanaka, H., Hosaka, Y.,
and Matsuda, M., (1994), Empirical study on
learning in fuzzy systems by rice test analysis,
Fuzzy Sets Syst., vol. 64, pp. 129-144.
Nozaki, K., Ishibuchi, H., and Tanaka, H., (1997),
A simple but powerful heuristic method for
generating fuzzy rules from numerical data, Fuzzy
Sets Syst., vol. 86, pp. 251-270.
Glorennec, P. Y., (1999), Algorithmes
d’apprentissage pour systems d’inference floue,
Paris, France: Hermes.
Wang, L. X. and Mendel, J. M., (1992),
Generating fuzzy rules by learning from examples,
IEEE Trans. Syst., Man, Cybern., vol. 22,
pp.1414-1427.
Quinlan, J. R., (1986), Induction of decision trees,
Mach. Learn., vol. 1, pp. 81-106.
Dunn, J. C., (1973), A fuzzy relative of the isodata
process and its use in detecting compact wellseparated clusters, J. Cybern., vol. 3, no. 3, pp. 32-
Matteucci, M., (2003), Fuzzy C-Means Clustering,
http://www.elet.polimi.it/upload/matteucc/Clusteri
ng/tutorial_html/cmeans.html, access: 30/04/06