Grouping of objects in a space of heterogeneous variables with the use of taxonomic decision trees

Authors:
V. B. Berikov
Affiliations:
Sobolev Institute of Mathematics, Siberian Branch, Russian Academy of Sciences, Novosibirsk, Russia 630090
Venue:
Pattern Recognition and Image Analysis
Year:
2011

Citing 2
Cited 0

Top-Down Induction of Clustering Trees

ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions

The Journal of Machine Learning Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

A problem of classification of objects in the presence of heterogeneous (qualitative, ordinal, nominal, and Boolean) variables is considered. Taxonomic decision trees are used to solve the problem. A quality criterion for a tree is introduced that is based on the Bayesian estimate of the Kullback-Leibler distance between distributions. Statistical modeling is applied to show the efficiency of an algorithm for constructing a tree that uses this criterion.