An Intelligent Knowledge Discovery System with a Novel Knowledge Acquisition Methodology
Qingzhang Chen, Chao Chen, Xiaoying Chen
Abstract
Qingzhang Chen, Chao Chen, Xiaoying Chen
Abstract
The rapid increase of raw data improves a large number of data mining algorithms. But this trend makes it difficult to choose suited data mining algorithms to discover valid knowledge in the data repositories for users with few prior experience of data mining. In this paper, we propose a framework named Intelligent Knowledge Discovery System (IKDS) which help users to select appropriate data mining algorithms to discover useful knowledge. A novel knowledge acquisition methodology called Simplified- EMCUD (SIMCAP) is proposes in IKDS for its Expert system to elicit explicit knowledge as well as implicit knowledge. Then we represent and store the knowledge in IKDS by XML. At last we give the prototypes of SIMCAP and IKDS.
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The rapid increase of raw data improves a large number of data mining algorithms. But this trend makes it difficult to choose suited data mining algorithms to discover valid knowledge in the data repositories for users with few prior experience of data mining. In this paper, we propose a framework named Intelligent Knowledge Discovery System (IKDS) which help users to select appropriate data mining algorithms to discover useful knowledge. A novel knowledge acquisition methodology called Simplified- EMCUD (SIMCAP) is proposes in IKDS for its Expert system to elicit explicit knowledge as well as implicit knowledge. Then we represent and store the knowledge in IKDS by XML. At last we give the prototypes of SIMCAP and IKDS.
Key concepts: Computer science, Knowledge extraction, Software mining, Knowledge acquisition, Knowledge-based systems, Data mining, Raw data, XML