Abstract
Decision tree learner inspect marginal class distributions of numerical attributes to infer a predicate that can be used as a decision node in the tree. Since such discretization techniques examine the marginal distribution only, they may fail completely to predict the class correctly even in cases for which a decision tree with a 100% classification rate exists. In this paper, an objective function-based clustering algorithm is modified to yield a discretization of numerical variables that overcomes these problems. The underlying clustering algorithm is the fuzzy c-means algorithm, which is modified to (a) take the class information into account and (b) to organize all cluster prototypes in a regular grid such that the grid rather than the individiual clusters are optimized.
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Höppner, F. (2006). Objective Function-based Discretization. In: Spiliopoulou, M., Kruse, R., Borgelt, C., Nürnberger, A., Gaul, W. (eds) From Data and Information Analysis to Knowledge Engineering. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31314-1_53
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DOI: https://doi.org/10.1007/3-540-31314-1_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31313-7
Online ISBN: 978-3-540-31314-4
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