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CIDM
2007
IEEE
9 years 4 months ago
A Prototype-driven Framework for Change Detection in Data Stream Classification
This paper presents a prototype-driven framework for classifying evolving data streams. Our framework uses cluster prototypes to summarize the data and to determine whether the cur...
Hamed Valizadegan, Pang-Ning Tan
ICML
2005
IEEE
9 years 11 months ago
A martingale framework for concept change detection in time-varying data streams
In a data streaming setting, data points are observed one by one. The concepts to be learned from the data points may change infinitely often as the data is streaming. In this pap...
Shen-Shyang Ho
ICDM
2007
IEEE
301views Data Mining» more  ICDM 2007»
9 years 2 months ago
Stream Event Detection: A Unified Framework for Mining Outlier, Change and Burst Simultaneously over Data Stream
Event detection is one of the most important issues of event processing system, especially Complex Event Processing (CEP). Outlier event, change event and burst event are three ty...
Zhijian Yuan, Kai Du, Yan Jia, Jiajia Miao
PKDD
2010
Springer
183views Data Mining» more  PKDD 2010»
8 years 8 months ago
Classification and Novel Class Detection of Data Streams in a Dynamic Feature Space
Data stream classification poses many challenges, most of which are not addressed by the state-of-the-art. We present DXMiner, which addresses four major challenges to data stream ...
Mohammad M. Masud, Qing Chen, Jing Gao, Latifur Kh...
IJCAI
2007
8 years 11 months ago
Detecting Changes in Unlabeled Data Streams Using Martingale
The martingale framework for detecting changes in data stream, currently only applicable to labeled data, is extended here to unlabeled data using clustering concept. The one-pass...
Shen-Shyang Ho, Harry Wechsler
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