@inproceedings{DBLP:conf/vldb/MorimotoFMTY98, author = {Yasuhiko Morimoto and Takeshi Fukuda and Hirofumi Matsuzawa and Takeshi Tokuyama and Kunikazu Yoda}, editor = {Ashish Gupta and Oded Shmueli and Jennifer Widom}, title = {Algorithms for Mining Association Rules for Binary Segmentations of Huge Categorical Databases}, booktitle = {VLDB'98, Proceedings of 24rd International Conference on Very Large Data Bases, August 24-27, 1998, New York City, New York, USA}, publisher = {Morgan Kaufmann}, year = {1998}, isbn = {1-55860-566-5}, pages = {380-391}, ee = {db/conf/vldb/MorimotoFMTY98.html}, crossref = {DBLP:conf/vldb/98}, bibsource = {DBLP, http://dblp.uni-trier.de} }BibTeX

We consider the problem of finding association rules that make nearly optimal binary segmentations of huge categorical databases. The optimality of segmentation is defined by an objective function suitable for the user's objective. An objective function is usually defined in terms of the distribution of agiven target attribute. Our goal is to find association rules that split databases into two subsets, optimizing the value of an objective function.

The problem is intractable for general objective functions, because letting *N* be the number of records of a given database, there are 2* ^{N}* possible binary segmentations, and we may have to exhaustively examine all of them. However, when the objective function is convex, there are feasible algorithms for finding nearly optimal binary segmentations, and we prove that typical criteria, such as "entropy (mutual information)," "

We propose practical algorithms that use computational geometry techniquesto handle cases where a target attribute is not binary, in which conventional approaches cannot be used directly.

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Contents BibTeX

- [AIS93]
- Rakesh Agrawal, Tomasz Imielinski, Arun N. Swami: Mining Association Rules between Sets of Items in Large Databases. SIGMOD Conference 1993: 207-216 BibTeX
- [AS94]
- Rakesh Agrawal, Ramakrishnan Srikant: Fast Algorithms for Mining Association Rules in Large Databases. VLDB 1994: 487-499 BibTeX
- [AT94]
- ...
- [BEHW89]
- Anselm Blumer, Andrzej Ehrenfeucht, David Haussler, Manfred K. Warmuth: Learnability and the Vapnik-Chervonenkis dimension. J. ACM 36(4): 929-965(1989) BibTeX
- [BFOS84]
- Leo Breiman, J. H. Friedman, R. A. Olshen, C. J. Stone:
Classification and Regression Trees.
Wadsworth 1984, ISBN 0-534-98053-8

BibTeX - [Cen92]
- ...
- [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: Constructing Efficient Decision Trees by Using Optimized Numeric Association Rules. VLDB 1996: 146-155 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
- [FMMT96c]
- Takeshi Fukuda, Yasuhiko Morimoto, Shinichi Morishita, Takeshi Tokuyama: Mining Optimized Association Rules for Numeric Attributes. PODS 1996: 182-191 BibTeX
- [HF95]
- Jiawei Han, Yongjian Fu: Discovery of Multiple-Level Association Rules from Large Databases. VLDB 1995: 420-431 BibTeX
- [HII95]
- Susumu Hasegawa, Hiroshi Imai, Masaki Ishiguro: epsilon-Approximations of k-Label Spaces. Theor. Comput. Sci. 137(1): 145-157(1995) BibTeX
- [HW87]
- ...
- [MFMT97]
- ...
- [MP91]
- ...
- [PCY95]
- Jong Soo Park, Ming-Syan Chen, Philip S. Yu: An Effective Hash Based Algorithm for Mining Association Rules. SIGMOD Conference 1995: 175-186 BibTeX
- [PS85]
- Franco P. Preparata, Michael Ian Shamos:
Computational Geometry - An Introduction.
Springer 1985, ISBN 3-540-96131-3

BibTeX - [PS91]
- Gregory Piatetsky-Shapiro: Discovery, Analysis, and Presentation of Strong Rules. Knowledge Discovery in Databases 1991: 229-248 BibTeX
- [Qui86]
- J. Ross Quinlan: Induction of Decision Trees. Machine Learning 1(1): 81-106(1986) BibTeX
- [Qui93]
- J. Ross Quinlan:
C4.5: Programs for Machine Learning.
Morgan Kaufmann 1993, ISBN 1-55860-238-0

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
- Johannes Gehrke, Raghu Ramakrishnan, Venkatesh Ganti: RainForest - A Framework for Fast Decision Tree Construction of Large Datasets. VLDB 1998: 416-427

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