Local Induction of Decision Trees: Towards Interactive Data Mining (1996)
| Venue: | Proceedings of the Second International Conference on Knowledge Discovery and Data Mining |
| Citations: | 5 - 0 self |
BibTeX
@INPROCEEDINGS{Fulton96localinduction,
author = {Truxton Fulton and Simon Kasif and Steven Salzberg and David Waltz},
title = {Local Induction of Decision Trees: Towards Interactive Data Mining},
booktitle = {Proceedings of the Second International Conference on Knowledge Discovery and Data Mining},
year = {1996},
pages = {14--19},
publisher = {AAAI Press}
}
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Abstract
Decision trees are an important data mining tool with many applications. Like many classification techniques, decision trees process the entire data base in order to produce a generalization of the data that can be used subsequently for classification. Large, complex data bases are not always amenable to such a global approach to generalization. This paper explores several methods for extracting data that is local to a query point, and then using the local data to build generalizations. These adaptively constructed neighborhoods can provide additional information about the query point. Three new algorithms are presented, and experiments using these algorithms are described. Keywords: Local Learning, Decision Trees, Data Mining. 1 Computer Science Dept., Johns Hopkins U., Baltimore, MD 2 NEC Research Institute, Princeton, NJ 08540 1 Introduction For any large, complex body of data, there is often a need to compute summaries and extract generalizations that characterize the data. D...
