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Abstract: Data mining algorithmsincluding machine learning, statistical analysis, and pattern recognition techniques can greatly improve our understanding of data warehouses that are now becoming more widespread. In this paper, we focus on classification algorithms and review the need for multiple classification algorithms. We describe a system called , which was designed to help choose the appropriate classification algorithm for a given dataset by making it easy to compare the utility of different... (Update)
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...distributions of the attributes and using their knowledge of the domain. Let us note that any segmentation method could be used [9]. After the beginning of this work, physicians noticed the origin of the unknown values: the explanation was a mistake during the moving of the...
.... data mining enviroments are WEKA 1 (Waikato Environment for Knowledge Analysis) 8] developed at University of Waikato, NZ, and MLC 2 [4], first developed at Stanford University, CA, USA, and then extended by Silicon Graphics, Inc. SGI) CA, USA. Weka is a collection of...
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BibTeX entry: (Update)
R. Kohavi, D. Sommerfield, and J. Dougherty, Data mining using MLC++ : A machine learning library in C ++ , in "Tools with Artificial Intelligence", pp. 234--245. IEEE Computer Society Press, 1996. See also http://www.sgi.com/Technology/mlc. http://citeseer.ist.psu.edu/kohavi96data.html More
@inproceedings{ kohavi96data,
author = "Ron Kohavi and Dan Sommerfield and James Dougherty",
title = "Data Mining Using {MLC}++: {A} Machine Learning Library in {C}++",
booktitle = "Tools with Artificial Intelligence",
publisher = "IEEE Computer Society Press",
pages = "234--245",
year = "1996",
url = "citeseer.ist.psu.edu/kohavi96data.html" }
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