| Summary: | 碩士 === 國立交通大學 === 資訊科學系 === 90 === Recently, the knowledge discovery system is a rapidly growing area of research. It is very difficult to discover valid knowledge in the data repositories and is also very difficult to choose suited data mining methods without prior knowledge about data mining or application domain since the amount of raw data becomes large and there are a variety of data mining methods.
In this thesis, we propose a framework of an Intelligent Knowledge Discovery System (IKDS) to help users select appropriate data mining algorithms and discover knowledge. In addition, a knowledge acquisition methodology, SEMCUD, is also proposed to elicit not only explicit knowledge but also implicit knowledge of the experts. The knowledge in IKDS can be represented and stored by XML. The prototypes of SEMCUD and IKDS have been built up to help users discover knowledge.
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