Detection of higher order covariate relations embedded in multi-dimensional data
| Project/Area Number |
19500130
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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 | Tokyo Denki University |
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Principal Investigator |
ICHINO Manabu Tokyo Denki University, 理工学部, 教授 (40057245)
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| Project Period (FY) |
2007 – 2009
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| Project Status |
Completed (Fiscal Year 2009)
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| Budget Amount *help |
¥2,600,000 (Direct Cost: ¥2,000,000、Indirect Cost: ¥600,000)
Fiscal Year 2009: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2008: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2007: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Keywords | データマイニング / シンボリック・データ解析 / 一般化相関係数 / 単調構造の検出 / 単調性の評価 / ヒストグラム / 主成分分析 / 分位数 / 知識発見 / パターン認識 / 共変性 / 近隣グラフ / 単調性 / パタン認識 / 親近性尺度 / 単調構造 |
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| Research Abstract |
This research aims to develop new methods applicable to detect monotone and locally monotone higher order covariate relations embedded in multidimensional symbolic data. We obtained the following three major results.
(1) Detection of locally monotonic chain structures embedded in multidimensional symbolic data : We developed a method that is able to detect higher order covariate relations. By this method we can detect higher order polynomial structures, sinusoidal structures, and others in multidimensional symbolic data table without functional identification process.
(2) A generalized correlation coefficient : By applying a well known correlation coefficient to local regions associated with each data sample and by aggregating the local correlations, we have a generalized correlation coefficient that is able to evaluate higher order covariate relations between two feature variables.
(3) The characterization of monotone structures by the nesting property and its application to symbolic data analysis : We frequently use histogram representations in order to reduce given huge data tables. By the virtue of monotone property of the cumulative distribution functions, we developed the quantile method to the principal component analysis for histogram valued data tables. The quantile method may also be able to treat other research problems in symbolic data analysis.
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Report
(4 results)
Research Products
(16 results)
