Multifaceted exploration of nonhomogeneous and ambiguous data by combining partial similarities
| Project/Area Number |
20700134
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
Intelligent informatics
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| Research Institution | Kyoto University |
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Principal Investigator |
TAKIGAWA Ichigaku Kyoto University, 化学研究所, 助教 (10374597)
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| Project Period (FY) |
2008 – 2010
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| Project Status |
Completed (Fiscal Year 2010)
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| Budget Amount *help |
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2010: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2009: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2008: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
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| Keywords | データマイニング / 部分構造探索 / アルゴリズム / 機械学習 / 部分構造検索 / 列挙アルゴリズム / 統計的有意性検定 / 統計的機械学習 |
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| Research Abstract |
Due to the computerization of industrial and scientific data, we can automatically collect or systematically obtain a huge dataset. Whereas these data are more easily accessible than before, their poor quality causes problems when we try to statistically analyze and utilize them. Many levels of information are collapsed into a single dataset since the purpose of use is ambiguous and unfixed in advance, and as a result, their quality is not sufficiently homogeneous. To address this problem, we developed novel statistical methods based on recently-emerged techniques for substructure enumeration, which can analyze those types of data by combining partial or local similarities in the data.
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Report
(4 results)
Research Products
(33 results)
