Extraction of developing type for construction of stochastic causality models from large scale historical data set
| Project/Area Number |
19500232
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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 |
Statistical science
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| Research Institution | Gunma University |
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Principal Investigator |
SEKI Yoichi Gunma University, 大学院・工学研究科, 教授 (90196949)
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| Co-Investigator(Kenkyū-buntansha) |
NAGAI Ayumu 群馬大学, 大学院・工学研究科, 助教 (70375567)
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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 |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2009: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2008: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2007: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | データマイニング / 統計数学 / 自己組織化 / 機械学習 / 回帰モデル / 介護医療保険サービス / アルゴリズム / モデル選択基準 / 介護保険サービス評価 |
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| Research Abstract |
We propose some methodology to extract individual clusters and developing type's models for construction of stochastic causality models from large scale historical data set. For clustering individual set, we make some cases using classical self-organizing maps, and propose flexible self-organizing maps, as a new method to present intrinsic topological structure of individuals. And we make some prediction models to estimate on the clusters.
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Report
(4 results)
Research Products
(20 results)
