A study on the analysis of big data with conversion to small data based on the principal points
| Project/Area Number |
16K16361
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Social systems engineering/Safety system
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| Research Institution | Sophia University (2017)
Waseda University (2016) |
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Principal Investigator |
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| Project Period (FY) |
2016-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥2,600,000 (Direct Cost: ¥2,000,000、Indirect Cost: ¥600,000)
Fiscal Year 2017: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2016: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | 主要点解析法 / ビジネスアナリティクス / クラスタリング / 経営工学 / 品質管理 / 機械学習 / 潜在クラスモデル / Principal Points / データ解析 / ビッグデータ解析 / 統計 |
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| Outline of Final Research Achievements |
This study focused on the big data that has the complex structure and also the number of variables are enormous. The main proposed models are 1) Multivariate clusterwise regression model and 2) Clustering analysis method considering the three mode data, and effective algorithms were proposed. We also expand the models that can be visualized. Moreover, we applied the methods to the real-world marketing data, and show the adequecy of the application.
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Report
(3 results)
Research Products
(17 results)
