標題: Generating learning sequences for decision makers through data mining and competence set expansion
作者: Hu, YC
Chen, RS
Tzeng, GH
科技管理研究所
資訊管理與財務金融系 註:原資管所+財金所
Institute of Management of Technology
Department of Information Management and Finance
關鍵字: competence set;data mining;decision making;fuzzy sets
公開日期: 1-Oct-2002
摘要: For each decision problem, there is a competence set, proposed by Yu, consisting of ideas, knowledge, information, and skills required for solving the problem. Thus, it is reasonable that we view a set of useful patterns discovered from a relational database by data mining techniques as a needed competence set for solving one problem. Significantly, when decision makers have not acquired the competence set, they may lack confidence in making decisions. In order to effectively acquire a needed competence set to cope with the corresponding problem, it is necessary to find appropriate learning sequences of acquiring those useful patterns, the so-called competence set expansion. This paper thus proposes an effective method consisting of two phases to generate learning sequences. The first phase finds a competence set consisting of useful patterns by using a proposed data mining technique. The other phase expands that competence set with minimum learning cost by the minimum spanning table method proposed by Feng and Yu. From a numerical example, we can see that it is possible to help decision makers to solve the decision problems by use of the data mining technique and the competence set expansion, enabling them to make better decisions.
URI: http://dx.doi.org/10.1109/TSMCB.2002.1033188
http://hdl.handle.net/11536/28481
ISSN: 1083-4419
DOI: 10.1109/TSMCB.2002.1033188
期刊: IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
Volume: 32
Issue: 5
起始頁: 679
結束頁: 686
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