Unsupervised On-line Learning of Decision Trees for Hierarchical Data Analysis

Part of Advances in Neural Information Processing Systems 10 (NIPS 1997)

Authors

Marcus Held, Joachim Buhmann

Abstract

An adaptive on-line algorithm is proposed to estimate hierarchical data structures for non-stationary data sources. The approach is based on the principle of minimum cross entropy to derive a decision tree for data clustering and it employs a metalearning idea (learning to learn) to adapt to changes in data characteristics. Its efficiency is demonstrated by grouping non-stationary artifical data and by hierarchical segmentation of LANDSAT images.