Paper Info
Title
Concept Drift Detection For Evolving Stream Data
Abstract
In processing stream data, time is one of the most significant facts not only because the size of data is dramatically increased but because the characteristics of data is varying over time. To learn stream data evolving over time effectively, it is required to detect the drift of concept. We present a window adaptation function on domain value (WAV) to determine the size of windowed batch for learning algorithms of stream data and a method to detect the change of data characteristics with a criterion function utilizing correlation. When applying our adaptation function to a clustering task on a multi-stream data model, the result of learning synopsis of windowed batch determined by it shows its effectiveness. Our criterion function with correlation information of value distribution over time can be the reasonable threshold to detect the change between windowed batches.
Year
DOI
Venue
2011
10.1587/transinf.E94.D.2288
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
Keywords
Field
DocType
stream, stream data, concept drift, change of the characteristics, clustering
Data mining,Data stream clustering,Pattern recognition,Computer science,Stream data,Concept drift,Correlation,Artificial intelligence,Cluster analysis,Criterion function,Data model
Journal
Volume
Issue
ISSN
E94D
11
0916-8532
Citations 
PageRank 
References 
0
0.34
4
Authors
2
Name
Order
Citations
PageRank
Jeong-Hoon Lee129116.06
Yoonjoon Lee2574175.37