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113
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KDD
2008
ACM
138views Data Mining» more  KDD 2008»
16 years 1 months ago
Quantitative evaluation of approximate frequent pattern mining algorithms
Traditional association mining algorithms use a strict definition of support that requires every item in a frequent itemset to occur in each supporting transaction. In real-life d...
Rohit Gupta, Gang Fang, Blayne Field, Michael Stei...
IDA
2007
Springer
15 years 20 days ago
Removing biases in unsupervised learning of sequential patterns
Unsupervised sequence learning is important to many applications. A learner is presented with unlabeled sequential data, and must discover sequential patterns that characterize th...
Yoav Horman, Gal A. Kaminka
133
Voted
EWMF
2003
Springer
15 years 6 months ago
Monitoring the Evolution of Web Usage Patterns
Abstract With the ongoing shift from off-line to on-line business processes, the Web has become an important business platform, and for most companies it is crucial to have an on-...
Steffan Baron, Myra Spiliopoulou
141
Voted
DATAMINE
1999
152views more  DATAMINE 1999»
15 years 12 days ago
Discovery of Frequent DATALOG Patterns
Discovery of frequent patterns has been studied in a variety of data mining settings. In its simplest form, known from association rule mining, the task is to discover all frequent...
Luc Dehaspe, Hannu Toivonen
182
Voted
ICIP
2010
IEEE
14 years 10 months ago
Building Emerging Pattern (EP) Random forest for recognition
The Random forest classifier comes to be the working horse for visual recognition community. It predicts the class label of an input data by aggregating the votes of multiple tree...
Liang Wang, Yizhou Wang, Debin Zhao