Development of probabilistic transition models using self-organizing of a large-scale histrical data on space-time dimension
| Project/Area Number |
23500344
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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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 群馬大学, 理工学研究科, 教授 (90196949)
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| Project Period (FY) |
2011 – 2013
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| Project Status |
Completed (Fiscal Year 2013)
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| Budget Amount *help |
¥5,200,000 (Direct Cost: ¥4,000,000、Indirect Cost: ¥1,200,000)
Fiscal Year 2013: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2012: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2011: ¥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 / 自己組織化 / SOM / 多項ロジットモデル / 医療保険サービス / ID付POS / 移動履歴 / 共起行列 / ＳＯＭ / 購買履歴データ / 統計数学 |
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| Research Abstract |
When we analyze a large-scale historical data on space-time dimension, there is the difficulty with the variety of the history. In this study, we proposed methodologies to develop probabilistic models of the transitions in histories, using their topological structure and the structure of the subsequence of history. We develop flexible self-organization map (FSOM), which is a SOM whose map range is automatically adjusted by the topological structure of history. Transitions in histories are grasped by the structure derived from the FSOM and so on. We verified our methodologies with the real world data, for example, service use history in merchandising or the finance business, health care service histories such as the receipt, and movement histories in city space.
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Report
(4 results)
Research Products
(23 results)
