NDLI: An Intelligent Knowledge Discovery System with a Novel Knowledge Acquisition Methodology

Content Provider	IEEE Xplore Digital Library
Author	Qingzhang Chen Chao Chen Xiaoying Chen
Copyright Year	2007
Description	Author affiliation: Zhejiang Univ. of Technol., Hangzhou (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.
Starting Page	322
Ending Page	327
File Size	274205
Page Count	6
File Format	PDF
ISBN	9780769528748
DOI	10.1109/FSKD.2007.168
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-24
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Chaos Databases Knowledge acquisition Prototypes XML Data warehouses Educational institutions Data mining Intelligent systems Expert systems
Content Type	Text
Resource Type	Article

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2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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An Intelligent Knowledge Discovery System with a Novel Knowledge Acquisition Methodology