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» Incremental Local Outlier Detection for Data Streams
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ASC
2008
13 years 7 months ago
Info-fuzzy algorithms for mining dynamic data streams
Most data mining algorithms assume static behavior of the incoming data. In the real world, the situation is different and most continuously collected data streams are generated by...
Lior Cohen, Gil Avrahami, Mark Last, Abraham Kande...
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...
LREC
2010
150views Education» more  LREC 2010»
13 years 8 months ago
Detection of Peculiar Examples using LOF and One Class SVM
This paper proposes the method to detect peculiar examples of the target word from a corpus. The peculiar example is regarded as an outlier in the given example set. Therefore we ...
Hiroyuki Shinnou, Minoru Sasaki
TNN
2008
178views more  TNN 2008»
13 years 7 months ago
IMORL: Incremental Multiple-Object Recognition and Localization
This paper proposes an incremental multiple-object recognition and localization (IMORL) method. The objective of IMORL is to adaptively learn multiple interesting objects in an ima...
Haibo He, Sheng Chen
ASC
2011
13 years 2 months ago
Handling drifts and shifts in on-line data streams with evolving fuzzy systems
In this paper, we present new approaches to handling drift and shift in on-line data streams with the help of evolving fuzzy systems (EFS), which are characterized by the fact tha...
Edwin Lughofer, Plamen P. Angelov