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PKDD
2005
Springer
101views Data Mining» more  PKDD 2005»
14 years 1 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 5 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...
CICLING
2010
Springer
14 years 2 months ago
Towards Automatic Detection and Tracking of Topic Change
We present an approach for automatic detection of topic change. Our approach is based on the analysis of statistical features of topics in time-sliced corpora and their dynamics ov...
Florian Holz, Sven Teresniak
ICDM
2006
IEEE
91views Data Mining» more  ICDM 2006»
14 years 1 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
ICPR
2010
IEEE
13 years 11 months ago
Inverse Multiple Instance Learning for Classifier Grids
Abstract--Recently, classifier grids have shown to be a considerable alternative for object detection from static cameras. However, one drawback of such approaches is drifting if a...
Sabine Sternig, Peter M. Roth, Horst Bischof