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» Mining Multiple Large Databases
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MM
2006
ACM
167views Multimedia» more  MM 2006»
15 years 8 months ago
Image annotation by large-scale content-based image retrieval
Image annotation has been an active research topic in recent years due to its potentially large impact on both image understanding and Web image search. In this paper, we target a...
Xirong Li, Le Chen, Lei Zhang, Fuzong Lin, Wei-Yin...
CIKM
2008
Springer
15 years 3 months ago
Viability of in-house datamarting approaches for population genetics analysis of snp genotypes
Background: Databases containing very large amounts of SNP (Single Nucleotide Polymorphism) data are now freely available for researchers interested in medical and/or population g...
Jorge Amigo, Christopher Phillips, Antonio Salas
DATAMINE
1999
108views more  DATAMINE 1999»
15 years 1 months ago
A Survey of Methods for Scaling Up Inductive Algorithms
Abstract. One of the de ning challenges for the KDD research community is to enable inductive learning algorithms to mine very large databases. This paper summarizes, categorizes, ...
Foster J. Provost, Venkateswarlu Kolluri
PAKDD
2004
ACM
199views Data Mining» more  PAKDD 2004»
15 years 7 months ago
Temporal Sequence Associations for Rare Events
In many real world applications, systematic analysis of rare events, such as credit card frauds and adverse drug reactions, is very important. Their low occurrence rate in large da...
Jie Chen, Hongxing He, Graham J. Williams, Huidong...
SIGMOD
2004
ACM
140views Database» more  SIGMOD 2004»
16 years 2 months ago
Incremental and Effective Data Summarization for Dynamic Hierarchical Clustering
Mining informative patterns from very large, dynamically changing databases poses numerous interesting challenges. Data summarizations (e.g., data bubbles) have been proposed to c...
Corrine Cheng, Jörg Sander, Samer Nassar