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ICDM
2009
IEEE
167views Data Mining» more  ICDM 2009»
13 years 3 months ago
Self-Adaptive Anytime Stream Clustering
Clustering streaming data requires algorithms which are capable of updating clustering results for the incoming data. As data is constantly arriving, time for processing is limited...
Philipp Kranen, Ira Assent, Corinna Baldauf, Thoma...
KDD
2006
ACM
129views Data Mining» more  KDD 2006»
14 years 6 months ago
Suppressing model overfitting in mining concept-drifting data streams
Mining data streams of changing class distributions is important for real-time business decision support. The stream classifier must evolve to reflect the current class distributi...
Haixun Wang, Jian Yin, Jian Pei, Philip S. Yu, Jef...
KDD
2008
ACM
239views Data Mining» more  KDD 2008»
14 years 6 months ago
Mining adaptively frequent closed unlabeled rooted trees in data streams
Closed patterns are powerful representatives of frequent patterns, since they eliminate redundant information. We propose a new approach for mining closed unlabeled rooted trees a...
Albert Bifet, Ricard Gavaldà
DATAMINE
2006
164views more  DATAMINE 2006»
13 years 5 months ago
Fast Distributed Outlier Detection in Mixed-Attribute Data Sets
Efficiently detecting outliers or anomalies is an important problem in many areas of science, medicine and information technology. Applications range from data cleaning to clinica...
Matthew Eric Otey, Amol Ghoting, Srinivasan Partha...
ICDM
2006
IEEE
91views Data Mining» more  ICDM 2006»
13 years 11 months ago
Entropy-based Concept Shift Detection
When monitoring sensory data (e.g., from a wearable device) the context oftentimes changes abruptly: people move from one situation (e.g., working quietly in their office) to ano...
Peter Vorburger, Abraham Bernstein