Concept mining system in multi-class KANSEI data
| Project/Area Number |
24700204
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Sensitivity informatics/Soft computing
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| Research Institution | Muroran Institute of Technology |
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Principal Investigator |
OKADA Yoshifumi 室蘭工業大学, 工学(系)研究科(研究院), 准教授 (00443177)
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| Project Period (FY) |
2012-04-01 – 2014-03-31
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| Project Status |
Completed (Fiscal Year 2013)
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| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2013: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
Fiscal Year 2012: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
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| Keywords | 多クラス / 概念 / バイクラスタリング / データマイニング |
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| Research Abstract |
The aim of this study was to develop a new concept mining method for multi-class dataset. The features of this method are that 1) concepts appearing specifically in each class are mined using the new biclustering method for multi-class data and that 2) only meaningful concepts are extracted on the basis of an ontology database. This study hereby made it possible not only to discover differentially-expressed useful concepts among classes, but also to reduce user's workload in selecting only necessary concepts from large amount of output concepts.
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Report
(3 results)
Research Products
(31 results)
