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» The UCI KDD Archive of Large Data Sets for Data Mining Resea...
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KDD
2008
ACM
140views Data Mining» more  KDD 2008»
14 years 5 months ago
Semi-supervised approach to rapid and reliable labeling of large data sets
Supervised classification methods have been shown to be very effective for a large number of applications. They require a training data set whose instances are labeled to indicate...
György J. Simon, Vipin Kumar, Zhi-Li Zhang
ISMIS
1999
Springer
13 years 9 months ago
Applications and Research Problems of Subgroup Mining
Knowledge Discovery in Databases (KDD) is a data analysis process which, in contrast to conventional data analysis, automatically generates and evaluates very many hypotheses, deal...
Willi Klösgen
KDD
2003
ACM
109views Data Mining» more  KDD 2003»
14 years 5 months ago
Experimental design for solicitation campaigns
Data mining techniques are routinely used by fundraisers to select those prospects from a large pool of candidates who are most likely to make a financial contribution. These tech...
Uwe F. Mayer, Armand Sarkissian
KDD
2002
ACM
150views Data Mining» more  KDD 2002»
14 years 5 months ago
Querying multiple sets of discovered rules
Rule mining is an important data mining task that has been applied to numerous real-world applications. Often a rule mining system generates a large number of rules and only a sma...
Alexander Tuzhilin, Bing Liu
KDD
2004
ACM
144views Data Mining» more  KDD 2004»
14 years 5 months ago
IncSpan: incremental mining of sequential patterns in large database
Many real life sequence databases, such as customer shopping sequences, medical treatment sequences, etc., grow incrementally. It is undesirable to mine sequential patterns from s...
Hong Cheng, Xifeng Yan, Jiawei Han