Algorithms for mining association rules for binary segmentations of huge categorical databases (1998)
| Venue: | Proc. of VLDB |
| Citations: | 24 - 1 self |
BibTeX
@INPROCEEDINGS{Morimoto98algorithmsfor,
author = {Yasuhiko Morimoto and Takeshi Fukuda and Hirofumi Matsuzawa and Takeshi Tokuyama and Kunikazu Yoda},
title = {Algorithms for mining association rules for binary segmentations of huge categorical databases},
booktitle = {Proc. of VLDB},
year = {1998},
pages = {380--391}
}
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Abstract
We consider the problem of finding association rules that make nearly optimal binary segmen-tations of huge categorical databases. The op-timality of segmentation is defined by an ob-jective function suitable for the user’s objec-tive. An objective function is usually defined in terms of the distribution of a given target attribute. Our goal is to find association rules that split databases into two subsets, optimiz-ing the value of an objective function. The problem is intractable for general ob-jective functions, because letting N be the number of records of a given database, there are 2N 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), ” “x2 (correla-tion), ” and “gini index (mean squared error),” are actually convex. We propose practical algorithms that use computational geometry techniques to handle
Keyphrases
association rule huge categorical database binary segmentation objective function typical criterion feasible algorithm target attribute gini index computational geometry technique practical algorithm ob-jective function suitable possible binary segmentation optimal binary segmentation general ob-jective function mutual information optimal binary segmen-tations
