Presentation | 1998/10/14 Algorithms for Mining Optimal Binary Segmentations for Categorical Attributes Takeshi Fukuda, Yasuhiko Morimoto, Takeshi Tokuyama, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | We consider the problem of finding nearly optimal binary segmentations of categorical databases. Our goal is to find tests against explanatory attributes that split databases into two subsets, optimizing the value of an objective function. The problem is intractable for general objective functions. However, when the objective function is convex, there are effective algorithms for finding nearly optimal binary segmentations, and typical criteria, such as "entropy(mutual information), " and "gini index(mean squared error), " are actually convex. We propose practical algorithms that use computational geometry techniques to handle cases where a target attribute is not binary, in which conventional approaches could not be used directly. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | data mining / segmentation / decision tree / computational geometry / randomized algorithm |
Paper # | DE98-23 |
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Committee | DE |
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Conference Date | 1998/10/14(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Data Engineering (DE) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Algorithms for Mining Optimal Binary Segmentations for Categorical Attributes |
Sub Title (in English) | |
Keyword(1) | data mining |
Keyword(2) | segmentation |
Keyword(3) | decision tree |
Keyword(4) | computational geometry |
Keyword(5) | randomized algorithm |
1st Author's Name | Takeshi Fukuda |
1st Author's Affiliation | IBM Tokyo Research Laboratory() |
2nd Author's Name | Yasuhiko Morimoto |
2nd Author's Affiliation | IBM Tokyo Research Laboratory |
3rd Author's Name | Takeshi Tokuyama |
3rd Author's Affiliation | IBM Tokyo Research Laboratory |
Date | 1998/10/14 |
Paper # | DE98-23 |
Volume (vol) | vol.98 |
Number (no) | 316 |
Page | pp.pp.- |
#Pages | 9 |
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