Study on Fuzzy Co-clustering from Large Scale Co-occurrence Data
Project/Area Number |
26330281
|
Research Category |
Grant-in-Aid for Scientific Research (C)
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Allocation Type | Multi-year Fund |
Section | 一般 |
Research Field |
Soft computing
|
Research Institution | Osaka Prefecture University |
Principal Investigator |
Honda Katsuhiro 大阪府立大学, 工学(系)研究科(研究院), 教授 (80332964)
|
Co-Investigator(Kenkyū-buntansha) |
野津 亮 大阪府立大学, 人間社会システム科学研究科, 准教授 (40405345)
生方 誠希 大阪府立大学, 工学(系)研究科(研究院), 助教 (10755698)
|
Project Period (FY) |
2014-04-01 – 2017-03-31
|
Project Status |
Completed (Fiscal Year 2016)
|
Budget Amount *help |
¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2016: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2015: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2014: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
|
Keywords | ファジィクラスタリング / 共クラスタリング / 意思決定支援 / 文書解析 / Webデータ解析 |
Outline of Final Research Achievements |
In this study, we tried to realize human-friendly intelligent information processing technologies through development of effective analysis techniques for documents and web data with co-clustering-based summarization of large scale co-occurrence data. In theoretical aspects, we achieved improvement of partition quality by adjusting intrinsic fuzziness of statistical co-clustering models and robustification of co-clustering models against outliers by introducing a noise rejection mechanism. In practical aspects, we achieved utilization of semi-supervision in Twitter document analysis and privacy preservation in eigen-face authentication with anonymization of personal information.
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Report
(4 results)
Research Products
(53 results)