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Collecting quality data for database mining

Zhang, Chengqi and Zhang, Shichao 2001, Collecting quality data for database mining, in AI 2001 : Advances in Artificial Intelligence : Proceedings of the 14th Australian Joint Conference on Artificial Intelligence, Australian Joint Conference on Artificial Intelligence, [Adelaide, S.Aust.], pp. 593-604, doi: 10.1007/3-540-45656-2_51.

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Title Collecting quality data for database mining
Author(s) Zhang, Chengqi
Zhang, Shichao
Conference name Australian Joint Conference on Artificial Intelligence (14th : 2001 : Adelaide)
Conference location Adelaide, S.Aust.
Conference dates 10-14 Dec. 2001
Title of proceedings AI 2001 : Advances in Artificial Intelligence : Proceedings of the 14th Australian Joint Conference on Artificial Intelligence
Editor(s) Stumptner, Markus
Corbett, Dan
Brooks, Mike
Publication date 2001
Start page 593
End page 604
Publisher Australian Joint Conference on Artificial Intelligence
Place of publication [Adelaide, S.Aust.]
Summary Data collecting is necessary to some organizations such as nuclear power plants and earthquake bureaus, which have very small databases. Traditional data collecting is to obtain necessary data from internal and external data-sources and join all data together to create a homogeneous huge database. Because collected data may be untrusty, it can disguise really useful patterns in data. In this paper, breaking away traditional data collecting mode that deals with internal and external data equally, we argue that the first step for utilizing external data is to identify quality data in data-sources for given mining tasks. Pre- and post-analysis techniques are thus advocated for generating quality data.
ISBN 3540429603
Language eng
DOI 10.1007/3-540-45656-2_51
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2001, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30004556

Document type: Conference Paper
Collection: School of Information Technology
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