| Project/Area Number |
16500084
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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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 | Hiroshima City University |
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Principal Investigator |
MIYAHARA Tetsuhiro Hiroshima City University, Faculty of Information Sciences, Associate. Professor, 情報科学部, 助教授 (90209932)
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| Co-Investigator(Kenkyū-buntansha) |
UCHIDA Tomoyuki Hiroshima City University, Faculty of Information Sciences, Associate Professor, 情報科学部, 助教授 (70264934)
SHOUDAI Takayoshi Kyushu University, Department of Informatics, Associate Professor, システム情報科学研究院, 助教授 (50226304)
HIROWATARI Eiju The University of Kitakyushu, Center for Fundamental Education, Associate Professor, 基盤教育センター, 助教授 (60274429)
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| Project Period (FY) |
2004 – 2006
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| Project Status |
Completed (Fiscal Year 2006)
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| Budget Amount *help |
¥3,200,000 (Direct Cost: ¥3,200,000)
Fiscal Year 2006: ¥1,000,000 (Direct Cost: ¥1,000,000)
Fiscal Year 2005: ¥1,000,000 (Direct Cost: ¥1,000,000)
Fiscal Year 2004: ¥1,200,000 (Direct Cost: ¥1,200,000)
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| Keywords | numerical data / graph structured data / data mining / recursive real-valued function |
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| Research Abstract |
The purpose of this research project is to give theoretical foundations of data mining from hybrid data with numerical attributes and graph structures. Since HTML/XML files are considered to be tree structured data, methods for discovering characteristic patterns from tree structured data are useful. Based on Genetic Programming, we have implemented a discovery system for characteristic tree structured patterns from given positive and negative examples of tree structured data. Our tree structured patterns are tag tree patterns. Although variables in a tag tree pattern are structured variables which can be substituted by arbitrary trees, these variables are considered to be special edges in a tree. Then we have naturally applied Genetic Programming, which is a genetic method for tree structured objects, to implementing our discovery system. Inferring real-valued functions from numerical data obtained from experiments or observations is a basic learning method for data mining from numerical data. A recursive real is a real number which we can deal with on a computer. So we have investigated learnabilities of recursive real-valued functions such as prediction and finite prediction of recursive real-valued functions. Also we have given various learning algorithms for tree or graph structured data, including an algorithm for extracting structural features among words and polynomial time inductive inference algorithms from positive data for newly introduced classes of graph languages.
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