| Project/Area Number |
26330033
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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 | University of Tsukuba |
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Principal Investigator |
SATO-ILIC Mika 筑波大学, システム情報系, 教授 (60269214)
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| Co-Investigator(Kenkyū-buntansha) |
青嶋 誠 筑波大学, 数理物質系, 教授 (90246679)
清水 信夫 統計数理研究所, データ科学研究系, 助教 (00332130)
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| Research Collaborator |
MARSALA Christophe University of Paris(UPMC), Department of Databases and Machine Learning, LIP6, 教授
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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: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2015: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2014: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
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| Keywords | 分類 / ビックデータ / 尺度構成 / 多次元尺度 / クラスター尺度 / 可視化手法 / 多次元尺度構成法 / クラスタリング / データマイニング / ファジィクラスタリング |
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| Outline of Final Research Achievements |
Multidimensional cluster scaling is developed as a novel method for big data analytics with an evaluation of its performance. The mainstream of the methodology for big data analytics includes the excluding data which has poor explainable power, reducing the data, and applying ordinary analytics. This methodology has a problem in relation to the validity of the result since the result depends on the criterion which determines the explainable power of the data. Therefore, in this study, the multidimensional cluster scaling was proposed in which all of the data information of the big data was used, and the data was analyzed in another space measured by another scale of the classification structure.
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