de Graaf, J., Kosters, W., & Witteman, J. (2001). Interesting fuzzy association rules in quantitative databases. In Principles of Data Mining and Copyright 2005, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. Home/Search Document Details and Download
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Evolving Fuzzy Decision Trees with Genetic Programming and.. - Eggermont (2002) (Correct)
....Warm) if variable i is continuous. In order to fuzzify the continuous attributes we rst have to cluster them into a nite number of clusters. 2. 2 Clustering and fuzzi cation We have chosen to use the same method of clustering and fuzzi cation as was used for fuzzy association rule mining in [5, 15]. This means that we will use a simple K means clustering algorithm for clustering the continuous predictor variables followed by fuzzi cation of the clusters. Although we could have used an evolutionary algorithm for clustering we decided on a K means clustering algorithm since it is fast, easy ....
J.M. de Graaf, W.A. Kosters, and J.J.W. Witteman. Interesting fuzzy association rules in quantitative databases. In L. de Raedt and Arno Siebes, editors, 5th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'01), volume 2168 of LNAI, pages 140-151. Springer Verlag, 2001.
Copyright 2005, Idea Group Inc. Copying or distributing in print.. - Idea (Correct)
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de Graaf, J., Kosters, W., & Witteman, J. (2001). Interesting fuzzy association rules in quantitative databases. In Principles of Data Mining and Copyright 2005, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited.
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