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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... (Update)
Context of citations to this paper: More
...layer etc. The error is back propogated throughout the network. An example of a system which uses a neural network approach is NeuroRule[5] where the number of input nodes corresponds to the dimensionality of the input tuples and the number of output nodes is equal to the...
.... of algorithms for frequent pattern discovery has turned out to be a popular topic in data mining (for a sample of algorithms, see [2, 3, 19, 30, 35]) Almost all algorithms are on some level based on the same idea of levelwise search, known in data mining from the Apriori...
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BibTeX entry: (Update)
H. Lu, R. Setiono, and H. Liu. NeuroRule: A Connectionist Approach to Data Mining. Proceedings of the 21th International Conference on Very Large Data Bases, pages 478--489, September 1995. http://citeseer.ist.psu.edu/lu95neurorule.html More
@inproceedings{ lu95neurorule,
author = "Hongjun Lu and Rudy Setiono and Huan Liu",
title = "NeuroRule: A Connectionist Approach to Data Mining",
booktitle = "The {VLDB} Journal",
pages = "478-489",
year = "1995",
url = "citeseer.ist.psu.edu/lu95neurorule.html" }
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650 Numerical methods for unconstrained optimization and nonline.. (context) - Jr, Schnabel - 1983
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88 Knowledge discovery in databases: An attribute oriented appr.. - Han, Cai et al. - 1992
56 Backpropagation algorithm which varies the number of hidden .. (context) - Hirose, Yamashita et al. - 1991
55 Symbolic and neural learning algorithms: An experimental com.. (context) - Shavlik, Mooney et al. - 1991
54 Neural Networks in Computer Intelligence (context) - Fu - 1994
48 Systems for knowledge discovery in databases - Matheus, Chan et al. - 1993
42 Learning algorithms for connectionist networks: Applied grad.. (context) - Watrous - 1987
33 Comparing connectionist and symbolic learning methods - Quinlan - 1994
22 Extracting rules from neural network by pruning and hidden u.. - Setiono - 1994
22 A penalty function approach for pruning feedforward neural n.. - Setiono - 1994
16 Improving the convergence of the backpropagation algorithm (context) - van Ooyen - 1992
7 A neural network construction algorithm which maximizes the .. (context) - Setiono - 1995
5 XR fast rule generator - rule, Proceedings et al. - 1995
3 special issue on learning and discovery in databases (context) - Cercone, Tsuchiya - 1993
2 Algorithm 500: Minimization of unconstrained multivariate fu.. (context) - Shanno, Phua - 1976
2 special isssue on knowledge discovery in databases (context) - Piatetsky-Shapiro - 1992
1 Guest editor introduction: Knowledge discovery in databases .. (context) - Piatetsky-Shapiro - 1995
1 Dynamic node creation in backpropgation networks (context) - Ash - 1989
1 An interval classifier for database mining approaches (context) - Agrawal, Ghosh et al. - 1992
1 Improving backpropgation learning with feature selection (context) - Setiono, Liu - 1995
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