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» Discovering Robust Knowledge from Databases that Change
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CIKM
2004
Springer
13 years 10 months ago
Discovering frequently changing structures from historical structural deltas of unordered XML
Recently, a large amount of work has been done in XML data mining. However, we observed that most of the existing works focus on the snapshot XML data, while XML data is dynamic i...
Qiankun Zhao, Sourav S. Bhowmick, Mukesh K. Mohani...
JASIS
2000
143views more  JASIS 2000»
13 years 4 months ago
Discovering knowledge from noisy databases using genetic programming
s In data mining, we emphasize the need for learning from huge, incomplete and imperfect data sets (Fayyad et al. 1996, Frawley et al. 1991, Piatetsky-Shapiro and Frawley, 1991). T...
Man Leung Wong, Kwong-Sak Leung, Jack C. Y. Cheng
RSCTC
1993
Springer
161views Fuzzy Logic» more  RSCTC 1993»
13 years 9 months ago
Quantifying Uncertainty of Knowledge Discovered From Databases
This paper focuses on the application of rough set constructs to inductive learning from a database. A design guideline is suggested, which provides users the option to choose app...
Yang Xiang, S. K. Michael Wong, Nick Cercone
ISCI
2007
151views more  ISCI 2007»
13 years 4 months ago
Enriching the ER model based on discovered association rules
The entity–relationship (ER) model, a powerful means for business and data modeling, needs to be enriched with new semantics as the real world changes and its understanding impr...
Guoqing Chen, Ming Ren, Peng Yan, Xunhua Guo
ESWA
2006
139views more  ESWA 2006»
13 years 4 months ago
An efficient data mining approach for discovering interesting knowledge from customer transactions
Mining association rules and mining sequential patterns both are to discover customer purchasing behaviors from a transaction database, such that the quality of business decision ...
Show-Jane Yen, Yue-Shi Lee