2001 Fiscal Year Final Research Report Summary
Knowledge Discovery in Databases
| Project/Area Number |
10143102
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| Research Category |
Grant-in-Aid for Scientific Research on Priority Areas (A)
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| Allocation Type | Single-year Grants |
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| Research Institution | The University of Tokyo |
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Principal Investigator |
MIYANO Satoru University of Tokyo, Institute of Medical Science, Professor, 医科学研究所, 教授 (50128104)
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| Co-Investigator(Kenkyū-buntansha) |
YAMAGUCHI Takahira Shizuoka University, Department of Information Science, Professor, 情報学部, 教授 (20174617)
MOTODA Hiroshi Osaka University, Institute for Industrial Sciences, Professor, 産業科学研究所, 教授 (00283804)
KITAGAWA Genshiro Institute for Statistical Mathematics, Department of Prediction and Control, Professor, 予測制御研究系, 教授 (20000218)
SUZUKI Einoshin Yokohama National University, Department of Computer Science, Associate Professor, 工学部, 助教授 (10251638)
MORISHITA Shinichi University of Tokyo, Graduate School of Frontier Sciences, Associate Professor, 新領域創成科学研究科, 助教授 (90292854)
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| Project Period (FY) |
1998 – 2000
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| Keywords | data mining / knowledge discovery / scientific database / machine learning / statistical science / rule discovery / wavelet analysis / artificial intelligence |
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| Research Abstract |
Scientists are struggling for creating better data, observations, and measurements in various fields. Their ultimate target is a discovery from such data. The process starting from data creation and ending with a series of discoveries is the object of our research. Thus the aim of our research is to create computational strategy for speeding up the discovery process in total. For this purpose, this project is organized with researchers working in scientific domains and researchers from computer science so that real issues in the discovery process will be exposed out and practical computational techniques will be devised and tested for solving these real issues.
Group A04 has succeeded in creating solutions to this problem and a bunch of novel methods with which the discovery process can be sped up efficiently. The contributions of this project can be summarized in three items :
1. Development of new methods/models for efficient/feasible processing in stages in computational discovery. All methods are developed for practice but most of them are well-abstracted enough for general purpose application.
2. Integration strategy for the total discovery process and paradigm of discovery system.
3. Discoveries
In addition to these individual research activities, this project took the initiatives in enlightenment of Discovery Science which are published as "S. Miyano (Guest Chief Editor), Surveys on Discovery Science, Special Issue, IEICE Transactions on Information and Systems, Vol. E83-D, No, 1, 2000" and "S. Morishita and S. Miyano (eds), Discovery Science and Data Mining, Kyoritsu Shuppan, 2000."
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