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PKDD
2005
Springer
101views Data Mining» more  PKDD 2005»
13 years 11 months ago
A Random Method for Quantifying Changing Distributions in Data Streams
In applications such as fraud and intrusion detection, it is of great interest to measure the evolving trends in the data. We consider the problem of quantifying changes between tw...
Haixun Wang, Jian Pei
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...
ICDE
2008
IEEE
137views Database» more  ICDE 2008»
14 years 6 months ago
Stop Chasing Trends: Discovering High Order Models in Evolving Data
Abstract-- Many applications are driven by evolving data -patterns in web traffic, program execution traces, network event logs, etc., are often non-stationary. Building prediction...
Shixi Chen, Haixun Wang, Shuigeng Zhou, Philip S. ...
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