| Project/Area Number |
23500283
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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 |
Sensitivity informatics/Soft computing
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| Research Institution | Osaka Prefecture University |
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Principal Investigator |
HONDA Katsuhiro 大阪府立大学, 工学(系)研究科(研究院), 教授 (80332964)
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| Co-Investigator(Kenkyū-buntansha) |
ICHIHASHI Hidetomo 大阪府立大学, 大学院・工学研究科, 教授 (30151476)
NOTSU Akira 大阪府立大学, 大学院・工学研究科, 准教授 (40405345)
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| Project Period (FY) |
2011 – 2013
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| Project Status |
Completed (Fiscal Year 2013)
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| Budget Amount *help |
¥5,070,000 (Direct Cost: ¥3,900,000、Indirect Cost: ¥1,170,000)
Fiscal Year 2013: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2012: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2011: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | ファジィ理論 / 共クラスタリング / 協調フィルタリング / 意思決定支援 / ファジィクラスタリング |
|---|
| Research Abstract |
Cooccurrence information analysis is an important technique for handling such cooccurrence information as purchase history data or document-keyword frequency data with the goal of extracting co-clusters of mutually familiar pairs of objects and items. In applying co-clustering algorithms to collaborative filtering and document summarization, it is needed to handle such data incompleteness as missing elements, noise and non-Euclidean nature. In this research, several co-clustering algorithms, which can handle incomplete cooccurrence information having intrinsic singular features, were studied and applied to personalized recommendation and document analysis for developing human-friendly intelligent information processing techniques.
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