Unsupervised On-line Learning of Decision Trees for Hierarchical Data Analysis
Marcus Held, Joachim M. Buhmann · 1997
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. 1 Introduction Unsupervised learning addresses the problem to detect structure inherent in unlabeled and unclassified data. The simplest, but not necessarily the best approach for extracting a grouping structure is to represent a set of data samples X = n x i 2 IR d ji = 1; : : : ; N o by a set of prototypes Y = n y ff 2 IR d jff = 1; : : : ; K o , K ø N . The encoding usually is represented by an assignment matrix M = (M iff ), where M iff = 1 if and only if x i belongs to cluster ff, and M iff = 0 otherwise. According to th...