| Project/Area Number |
18500090
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Media informatics/Database
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| Research Institution | Future University-Hakodate |
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Principal Investigator |
KONISHI Osamu Future University-Hakodate, Dept. of Complex Systems, Professor (00135386)
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| Co-Investigator(Kenkyū-buntansha) |
NIIMI Ayahiko Future Univ.-Hakodate, Dept. of Complex Systems, Lecturer (80347179)
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| Project Period (FY) |
2006 – 2007
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| Project Status |
Completed (Fiscal Year 2007)
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| Budget Amount *help |
¥3,950,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2007: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2006: ¥2,000,000 (Direct Cost: ¥2,000,000)
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| Keywords | Information Integration / Emergent Systems / Probabilistic Information Processing / Distributed Co-operative Processing / Immune Systems / Sensor Mining / Complex Networks / Data Mining Systems / データベース / データマイニング / センサ・マイニング / ナレッジグリッド / P2P / 創発システム / エージェント |
|---|
| Research Abstract |
Developing intelligent tools for the integration of information extracted from multiple heterogeneous sources is a challenging issue to effectively exploit the numerous and dynamic sources available on-line in global information systems. Aggregated, summarized or symbolized information is more useful for understanding complex phenomena than raw information. Such information integration will be brought by novel methods for data mining and information extraction specifically tailored to networked information systems.
In this research we proposed an adaptive data mining system as the special kind of information system. These novel methods for data mining are based on different approaches, including theoretical, constructive, and experimental approaches :
(1)immune algorithms with memory organization for immunity.
(2)kernel methods, boosting as probabilistic information processing
(3)agent based distributed machine learning in sensor databases
(4)graph mining based on complex network theory
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