2012 Fiscal Year Final Research Report
Multi-task Data Mining Based on Dynamic Representation Bias
| Project/Area Number |
21300053
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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 | Kyushu University |
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Principal Investigator |
SUZUKI Einoshin 九州大学, システム情報科学研究院, 教授 (10251638)
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| Co-Investigator(Renkei-kenkyūsha) |
TONG Bin 九州大学, システム生命科学府, 博士課程学生
SHAO Hao 九州大学, システム生命科学府, 博士課程学生
NGUYEN Huy Thach 九州大学, システム生命科学府, 博士課程学生
SUGAYA Shinsuke 九州大学, システム情報科学府, 博士課程学生
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| Project Period (FY) |
2009 – 2012
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| Keywords | 動的表現バイアス / マルチタスクデータマイニング / 分類学習 / クラスタリング |
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| Research Abstract |
To effectively cope with multiple pattern discovery tasks related each other, we have developed novel data mining methods each of which automatically modifies representations of data and patterns, implemented them as computer systems, and demonstrated their effectiveness with synthetic and real data. Remarkable achievements are multi-task classification method which employs an extended MDL principle to allow a common dictionary, a multi-task clustering method which employs an extension of information distance based on Kolmogorov complexity, and a dimension reduction method for multi-task data mining.
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