Unsupervised Discretization Using Tree-Based Density Estimation

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.

• Type: Conference
• Year: 2005
• Where: PKDD
• Authors: Gabi Schmidberger, Eibe Frank
• Keywords: Discretization, Equal-width Discretization, PKDD 2005, Unsupervised Discretization Method