@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} }BibTeX

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 BibTeX

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

- [ACKT96]
- Tetsuo Asano, Danny Z. Chen, Naoki Katoh, Takeshi Tokuyama: Polynomial-Time Solutions to Image Segmentation. SODA 1996: 104-113 BibTeX
- [AGL+92]
- Rakesh Agrawal, Sakti P. Ghosh, Tomasz Imielinski, Balakrishna R. Iyer, Arun N. Swami: An Interval Classifier for Database Mining Applications. VLDB 1992: 560-573 BibTeX
- [ALS93]
- Rakesh Agrawal, Tomasz Imielinski, Arun N. Swami: Database Mining: A Performance Perspective. IEEE Trans. Knowl. Data Eng. 5(6): 914-925(1993) BibTeX
- [AT94]
- ...
- [BFOS84]
- ...
- [CG86]
- Bernard Chazelle, Leonidas J. Guibas: Fractional Cascading: I. A Data Structuring Technique. Algorithmica 1(2): 133-162(1986) BibTeX
- [DE93]
- David P. Dobkin, David Eppstein: Computing the Discrepancy. Symposium on Computational Geometry 1993: 47-52 BibTeX
- [DEY86]
- David P. Dobkin, Herbert Edelsbrunner, Chee-Keng Yap: Probing Convex Polytopes. STOC 1986: 424-432 BibTeX
- [FMMT96a]
- Takeshi Fukuda, Yasuhiko Morimoto, Shinichi Morishita, Takeshi Tokuyama: Mining Optimized Association Rules for Numeric Attributes. PODS 1996: 182-191 BibTeX
- [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 BibTeX
- [FMMY96c]
- Takeshi Fukuda, Yasuhiko Morimoto, Shinichi Morishita, Takeshi Tokuyama: SONAR: System for Optimized Numeric AssociationRules. SIGMOD Conference 1996: 553 BibTeX
- [GJ79]
- M. R. Garey, David S. Johnson:
Computers and Intractability: A Guide to the Theory of NP-Completeness.
W. H. Freeman 1979, ISBN 0-7167-1044-7

BibTeX - [HR76]
- Laurent Hyafil, Ronald L. Rivest: Constructing Optimal Binary Decision Trees is NP-Complete. Inf. Process. Lett. 5(1): 15-17(1976) BibTeX
- [MAR96]
- Manish Mehta, Rakesh Agrawal, Jorma Rissanen: SLIQ: A Fast Scalable Classifier for Data Mining. EDBT 1996: 18-32 BibTeX
- [MST84]
- ...
- [PR90]
- ...
- [Q86]
- J. Ross Quinlan: Induction of Decision Trees. Machine Learning 1(1): 81-106(1986) BibTeX
- [Q93]
- J. Ross Quinlan:
C4.5: Programs for Machine Learning.
Morgan Kaufmann 1993, ISBN 1-55860-238-0

BibTeX - [QR89]
- J. Ross Quinlan, Ronald L. Rivest: Inferring Decision Trees Using the Minimum Description Length Principle. Inf. Comput. 80(3): 227-248(1989) BibTeX

- Shinichi Morishita, Jun Sese: Traversing Itemset Lattice with Statistical Metric Pruning. PODS 2000: 226-236
- Johannes Gehrke, Venkatesh Ganti, Raghu Ramakrishnan, Wei-Yin Loh: BOAT-Optimistic Decision Tree Construction. SIGMOD Conference 1999: 169-180
- Rajeev Rastogi, Kyuseok Shim: PUBLIC: A Decision Tree Classifier that Integrates Building and Pruning. VLDB 1998: 404-415
- Yasuhiko Morimoto, Takeshi Fukuda, Hirofumi Matsuzawa, Takeshi Tokuyama, Kunikazu Yoda: Algorithms for Mining Association Rules for Binary Segmentations of Huge Categorical Databases. VLDB 1998: 380-391
- Johannes Gehrke, Raghu Ramakrishnan, Venkatesh Ganti: RainForest - A Framework for Fast Decision Tree Construction of Large Datasets. VLDB 1998: 416-427
- Takeshi Fukuda, Hirofumi Matsuzawa: Parallel Processing of Multiple Aggregate Queries on Shared-Nothing Multiprocessors. EDBT 1998: 278-292
- Yasuhiko Morimoto, Hiromu Ishii, Shinichi Morishita: Efficient Construction of Regression Trees with Range and Region Splitting. VLDB 1997: 166-175

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