| Project/Area Number |
21300047
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | Hokkaido University |
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Principal Investigator |
HARAGUCHI Makoto 北海道大学, 大学院・情報科学研究科, 教授 (40128450)
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|---|
| Co-Investigator(Kenkyū-buntansha) |
TOMITA Etsuji 電気通信大学, 名誉教授 (40016598)
OKUBO Yoshiaki 北海道大学, 大学院・情報科学研究科, 助教 (40271639)
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| Project Period (FY) |
2009 – 2011
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| Project Status |
Completed (Fiscal Year 2011)
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| Budget Amount *help |
¥6,500,000 (Direct Cost: ¥5,000,000、Indirect Cost: ¥1,500,000)
Fiscal Year 2011: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2010: ¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
Fiscal Year 2009: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | 変化検出問題 / 情報量差分 / 制約マイニング / 情報量変化 / クリーク枚挙 / 組み合わせ最適化 / クラスタリング / クラスター構造 / 分枝限定法 / クリーク制約 / Emerging Pattern / パターンの摂動 / 稀少パターン発見 |
|---|
| Research Abstract |
We have targeted patterns of variables whose correlations get increased after an event, while the correlations are uncorrelated before the event. To take into account positive, negative and even partial correlations among variables, we adopt Kullback-Leibler divergence for two contexts, before and after the event, and take their difference as the measure of changes. To reduce the computational cost for set partitioning, we implemented two strategies, Double Clique Constraints and Jumping Emerging Correlation Change, and showed that the correlation change problem can be solved efficiently even for large data sets.
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