| Project/Area Number |
26330277
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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 |
Soft computing
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| Research Institution | Kyushu Institute of Technology |
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Principal Investigator |
SAKAI Hiroshi 九州工業大学, 大学院工学研究院, 教授 (60201513)
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| Research Collaborator |
NAKATA Michinori 城西国際大学, 経営情報学科, 教授
Shen Kao-Yi Chinese Culture University, Department of Banking and Finance, Associate Professor
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| Project Period (FY) |
2014-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2017: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2016: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2015: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2014: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Keywords | ラフ集合 / 非決定情報 / アプリオリアルゴリズム / NIS-アプリオリ / データマイニング / 相関ルール / 欠損値 / SQL / 不完全情報 / 可能世界意味論 / 欠損値推定 / ラフ集合非決定情報解析 / NIS-アプリオリアルゴリズム / ルール生成による機械学習 / プライバシー保護 / 情報の希薄化 / NIS-Apriori / 粒状計算 / 少数派ルールマイニング / ラフ集合欠損値推定 / ラフ集合機械学習 / 並列化NIS-Aprioriアルゴリズム / NIS-Aprioriアルゴリズムの完全性 |
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| Outline of Final Research Achievements |
Rough set theory is a mathematical framework for mining table data sets. This theory is utilized for generating the characteristic implications (rules), and is applied to the recognition of the properties and decision support in table data sets. Principal investigator introduced the modal logic (possible worlds semantics) in rough set theory, and proposed Rough set Non-deterministic Information Analysis (RNIA) that can be taken into account to non-deterministic information. Because, the computational complexity problem was solved, RNIA became a quite unique framework. The proposed NIS-Apriori algorithm is employed as the core algorithm, and related problems like minor rule mining, the improvement of the analytic software tool, the improvement toward big data analysis, the analysis of the actual data sets, privacy-preserving data mining, the estimation of missing values, were solved by the NIS-Apriori algorithm.
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