| Project/Area Number |
10480066
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| Research Category |
Grant-in-Aid for Scientific Research (B).
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | UNIVERSITY OF TOKYO |
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Principal Investigator |
MORISHITA Shinichi GRADUATE SCHOOL OF FRONTIER SCIENCE, UNIVERSITY OF TOKYO, Associate Professor, 大学院・新領域創成科学研究科, 助教授 (90292854)
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| Co-Investigator(Kenkyū-buntansha) |
中谷 明弘 東京大学, 医科学研究所, 寄付研究部門教員 (60301149)
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| Project Period (FY) |
1998 – 2000
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| Project Status |
Completed (Fiscal Year 2000)
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| Budget Amount *help |
¥10,400,000 (Direct Cost: ¥10,400,000)
Fiscal Year 2000: ¥1,500,000 (Direct Cost: ¥1,500,000)
Fiscal Year 1999: ¥5,000,000 (Direct Cost: ¥5,000,000)
Fiscal Year 1998: ¥3,900,000 (Direct Cost: ¥3,900,000)
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| Keywords | DATA MINING / DATABASE QUERY OPTIMIZATION / ALGORITHM / COMPUTATIONAL COMPLEXITY / 知識発見 |
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| Research Abstract |
We have studied how to efficiently compute significant association rules according to common statistical measures such as a chi-squared value or correlation coefficient. For this purpose, one might consider to use of the Apriori algorithm, but the algorithm needs major conversion, because none of these statistical metrics are anti-monotone, and the use of higher support for reducing the search space cannot guarantee solutions in its the search space. We have developed a method of estimating a tight upper bound on the statistical metric associated with any superset of an itemset, as well as the novel use of the resulting information of upper bounds to prune unproductive supersets while traversing itemset lattices. Experimental tests demonstrated the efficiency of this method.
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