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» Capturing the Sudden Concept Drift in Process Mining
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MSR
2009
ACM
13 years 11 months ago
Tracking concept drift of software projects using defect prediction quality
Defect prediction is an important task in the mining of software repositories, but the quality of predictions varies strongly within and across software projects. In this paper we...
Jayalath Ekanayake, Jonas Tappolet, Harald Gall, A...
ASC
2008
13 years 5 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...
SEMWEB
2007
Springer
13 years 11 months ago
DRIFT: A Framework for Ontology-based Design Support Systems
This paper proposes a framework for ontology-based design support systems, called DRIFT (Design Rationale Integration Framework of Three layers), which records, structures and retr...
Yutaka Nomaguchi, Kikuo Fujita
PKDD
2005
Springer
101views Data Mining» more  PKDD 2005»
13 years 10 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
KDD
2009
ACM
187views Data Mining» more  KDD 2009»
14 years 5 months ago
New ensemble methods for evolving data streams
Advanced analysis of data streams is quickly becoming a key area of data mining research as the number of applications demanding such processing increases. Online mining when such...
Albert Bifet, Bernhard Pfahringer, Geoffrey Holmes...