| Project/Area Number |
16K00298
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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 |
Intelligent informatics
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| Research Institution | University of Yamanashi |
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Principal Investigator |
IWANUMA Koji 山梨大学, 大学院総合研究部, 教授 (30176557)
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| Co-Investigator(Kenkyū-buntansha) |
山本 泰生 静岡大学, 情報学部, 准教授 (30550793)
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| Project Period (FY) |
2016-04-01 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2018: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2017: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2016: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Keywords | データマイニング / 潜在的規則 / 負の相関ルール / 圧縮 / 極小生成子 / オンライン計算 / 一般化アイテム集合 / 一般化相関ルール / 潜在的規則発見 / 拡張FP木 / オンライン型アルゴリズム / 近似計算 / 飽和集合 / アイテム集合系列 / オンライン型計算 / 仮説推論 |
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| Outline of Final Research Achievements |
In this research, we proposed several new methods for online mining of negative association rules in order to discover important latent rules embedding in a data stream. Moreover, we gave a new framework, called a generalized association rule, for treating positive and negative association rules in a uniform way, and studied a hypothetical reasoning based on this proposed framework.
More concretely, we first proposed a novel effective lossless compression method of negative association rules by using minimal generators, and gave a fast algorithm for extracting minimal generators from closed itemsets (we got the 2017 research award from Japan Artificial Intelligence Association). Next we construct an efficient compression methods for online mining of closed itemsets in a data stream. Furthermore, we proposed a concept of generalized itemsets consisting of both positive and negative items, and studied a fast extraction method of the generalized itemsets for a transaction database.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究の学術的意義は，これまで殆ど着目されてこなかった負の相関ルールと潜在的規則関係に着目し，種々の技術的開発を行ったことにある．これにより巨大データに埋もれる多数の潜在的性質を，負ルールの形で抽出発見することがある程度可能になった．昨今のセンサーネットワークの飛躍的な発展に伴い増大する一方の巨大データの利用方法を，より一層高度化し深化をさせる試みであり，昨今の高度情報化社会における社会的貢献を行うものと考えられる．
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