| Project/Area Number |
26330052
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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 | Okayama University of Science |
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Principal Investigator |
Mori Yuichi 岡山理科大学, 総合情報学部, 教授 (80230085)
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| Co-Investigator(Kenkyū-buntansha) |
飯塚 誠也 岡山大学, 全学教育・学生支援機構, 教授 (60322236)
黒田 正博 岡山理科大学, 総合情報学部, 教授 (90279042)
水谷 直樹 岡山理科大学, 総合情報学部, 准教授 (30330533)
久保田 貴文 多摩大学, 経営情報学部, 准教授 (30379705)
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| Co-Investigator(Renkei-kenkyūsha) |
Adachi Kohei 大阪大学, 人間科学研究科, 教授 (60299055)
Nakano Junji 統計数理研究所, 統計計算開発センター, 教授 (60136281)
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| Project Period (FY) |
2014-04-01 – 2017-03-31
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| Project Status |
Completed (Fiscal Year 2016)
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| Budget Amount *help |
¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2016: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2015: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
Fiscal Year 2014: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
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| Keywords | 多変量解析 / 次元縮約 / 非計量主成分分析 / 非計量因子分析 / クラスター分析 / 加速化アルゴリズム / 対話的可視化ツール |
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| Outline of Final Research Achievements |
The purpose of this study is to propose joint dimension reduction and clustering to deeply consider the features of data, particularly large-scales data. To do this, the followings are performed: gathering information in previous studies, reviewing dimension reduction (including variable selection), proposing dimension reduction and clustering dealing with mixed measurement level data, developing interactive interface, and improving computational efficiency. The study is successful to provide some methods that handle the complexity including mixture of measurement level in the context of principal components analysis, and that consider the features of data by heuristic thinking and trial and error using developed interactive interfaces and proposed acceleration algorithms in case where a huge number of computation is necessary such as large-scaled data processing and variable selection.
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