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Abstract: Distance is used as a measurement in most of instance-based classification systems. Rather than using distance, we make use of the frequency of an instance's subsets and the frequency-changing rate of the subsets among training classes to perform both knowledge discovery and classification tasks. We name the system DeEPs. Whenever an instance is considered, DeEPs can efficiently discover those patterns contained in the instance which sharply differentiate the training classes from one to... (Update)
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BibTeX entry: (Update)
Li, J., Dong, G., Ramamohanaro, K. and Wong, L. (2001). DeEPs: A new instance-based discovery and classification system. [http://sdmc.krdl.org.sg:8080/limsoon/limsoonpapers.html], School of Computing, National University of Singapore. http://citeseer.ist.psu.edu/li01deeps.html More
@misc{ li01deeps,
author = "J. Li and G. Dong and K. Ramamohanaro and L. Wong",
title = "DeEPs: A new instance-based discovery and classification system",
text = "Li, J., Dong, G., Ramamohanaro, K. and Wong, L. (2001). DeEPs: A new instance-based
discovery and classification system. [http://sdmc.krdl.org.sg:8080/limsoon/limsoonpapers.html],
School of Computing, National University of Singapore.",
year = "2001",
url = "citeseer.ist.psu.edu/li01deeps.html" }
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73 Mining the most interesting rules (context) - Bayardo, Agrawal - 1999
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