@inproceedings{DBLP:conf/vldb/FukudaMMT96, author = {Takeshi Fukuda and Yasuhiko Morimoto and Shinichi Morishita and Takeshi Tokuyama}, editor = {T. M. Vijayaraman and Alejandro P. Buchmann and C. Mohan and Nandlal L. Sarda}, title = {Constructing Efficient Decision Trees by Using Optimized Numeric Association Rules}, booktitle = {VLDB'96, Proceedings of 22th International Conference on Very Large Data Bases, September 3-6, 1996, Mumbai (Bombay), India}, publisher = {Morgan Kaufmann}, year = {1996}, isbn = {1-55860-382-4}, pages = {146-155}, ee = {db/conf/vldb/FukudaMMT96.html}, crossref = {DBLP:conf/vldb/96}, bibsource = {DBLP, http://dblp.uni-trier.de} }

We propose an extension of an entropy-based heuristic of
Quinlan [Q93] for constructing a decision tree from a
large database with many numeric attributes.
Quinlan pointed out that his original method
(as well as other existing methods) may be inefficient if
any numeric attributes are strongly correlated.
Our approach offers one solution to this problem.
For each pair of numeric attributes with strong correlation,
we compute a two-dimensional association rule with respect to
these attributes and the objective attribute of the decision tree.
In particular, we consider a family *R* of grid-regions
in the plane associated with the pair of attributes.
For R in *R*, the data can be
split into two classes: data inside R and data outside R.

We compute the region Ropt in *R*
that minimizes the entropy of the splitting,
and add the splitting associated with Ropt
(for each pair of strongly correlated attributes) to
the set of candidate tests in Quinlan's entropy-based heuristic.

We give efficient algorithms for cases in which *R* is
(1) x-monotone connected regions,
(2) based-monotone regions,
(3) rectangles, and
(4) rectilinear convex regions.
The algorithm for the first case
has been implemented as a subsystem of
SONAR(System for Optimized Numeric Association Rules) developed by the
authors.
Tests show that our approach can create small-sized decision trees.

*Copyright © 1996 by the VLDB Endowment.
Permission to copy without fee all or part of this material is granted provided that the copies are not made or
distributed for direct commercial advantage, the VLDB
copyright notice and the title of the publication and
its date appear, and notice is given that copying
is by the permission of the Very Large Data Base
Endowment. To copy otherwise, or to republish, requires
a fee and/or special permission from the Endowment.*

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Contents

- From SunSITE Central Europe (Aachen, Germany)
- From CS Dept., University Trier (Germany)

- [ACKT96]
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- [FMMT96a]
- Takeshi Fukuda, Yasuhiko Morimoto, Shinichi Morishita, Takeshi Tokuyama: Mining Optimized Association Rules for Numeric Attributes. PODS 1996: 182-191
- [FMMT96b]
- Takeshi Fukuda, Yasuhiko Morimoto, Shinichi Morishita, Takeshi Tokuyama: Data Mining Using Two-Dimensional Optimized Accociation Rules: Scheme, Algorithms, and Visualization. SIGMOD Conference 1996: 13-23
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