DocumentCode :
3287267
Title :
Expert-Driven Knowledge Discovery
Author :
Ling, Tristan ; Kang, Byeong Ho ; Johns, David P. ; Walls, Justin ; Bindoff, Ivan
Author_Institution :
Univ. of Tasmania, Hobart
fYear :
2008
fDate :
7-9 April 2008
Firstpage :
174
Lastpage :
178
Abstract :
Knowledge discovery techniques find new knowledge about a domain by analysing existing domain knowledge and examples of domain data. These techniques typically involve using a human expert and automated software analysis (data mining). Often the human expertise is used initially to choose which data is processed, and then finally to determine which results are relevant. However studies have noted that some domains contain data stores too extensive and detailed, and existing knowledge too complex, for effective data selection or efficient data mining. A different approach is suggested which involves the human expert more pervasively, taking advantage of their expertise at each step, while using data mining techniques to assist in discovering data trends and in verifying the expert´s findings. Preliminary results suggest that the approach can be successfully applied to discover new knowledge in a complex domain, and reveal many potential areas for research and development.
Keywords :
data mining; research and development; automated software analysis; data mining; data processing; data selection; expert-driven knowledge discovery; human expert; research and development; Australia; Costs; Data analysis; Data mining; Humans; Information analysis; Information technology; Knowledge acquisition; Prototypes; Research and development; Data Mining; Expert; Knowledge Acquisition; Knowledge Discovery;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Technology: New Generations, 2008. ITNG 2008. Fifth International Conference on
Conference_Location :
Las Vegas, NV
Print_ISBN :
0-7695-3099-0
Type :
conf
DOI :
10.1109/ITNG.2008.194
Filename :
4492474
Link To Document :
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