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» Detecting Changes in Unlabeled Data Streams Using Martingale
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IJCAI
2007
8 years 8 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
ICML
2005
IEEE
9 years 8 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
KDD
2008
ACM
239views Data Mining» more  KDD 2008»
9 years 7 months ago
Mining adaptively frequent closed unlabeled rooted trees in data streams
Closed patterns are powerful representatives of frequent patterns, since they eliminate redundant information. We propose a new approach for mining closed unlabeled rooted trees a...
Albert Bifet, Ricard Gavaldà
RAID
2009
Springer
9 years 1 months ago
Autonomic Intrusion Detection System
Abstract. We propose a novel framework of autonomic intrusion detection that fulfills online and adaptive intrusion detection in unlabeled audit data streams. The framework owns a...
Wei Wang 0012, Thomas Guyet, Svein J. Knapskog
SDM
2009
SIAM
129views Data Mining» more  SDM 2009»
9 years 4 months ago
Scalable Distributed Change Detection from Astronomy Data Streams Using Local, Asynchronous Eigen Monitoring Algorithms.
This paper considers the problem of change detection using local distributed eigen monitoring algorithms for next generation of astronomy petascale data pipelines such as the Larg...
Kamalika Das, Kanishka Bhaduri, Sugandha Arora, We...
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