Unsupervised discretization using tree-based density estimation

Authors:
Gabi Schmidberger;Eibe Frank
Affiliations:
Department of Computer Science, University of Waikato, Hamilton, New Zealand;Department of Computer Science, University of Waikato, Hamilton, New Zealand
Venue:
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Year:
2005

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Abstract

This paper presents an unsupervised discretization method that performs density estimation for univariate data. The subintervals that the discretization produces can be used as the bins of a histogram. Histograms are a very simple and broadly understood means for displaying data, and our method automatically adapts bin widths to the data. It uses the log-likelihood as the scoring function to select cut points and the cross-validated log-likelihood to select the number of intervals. We compare this method with equal-width discretization where we also select the number of bins using the cross-validated log-likelihood and with equal-frequency discretization.