DocumentCode :
2763794
Title :
Foundations of Adaptive Data Stream Mining for Mobile and Embedded Applications
Author :
Gaber, Mohamed Medhat
Author_Institution :
Centre for Distrib. Syst. & Software Eng., Monash Univ., Melbourne, VIC
fYear :
2008
fDate :
18-20 Dec. 2008
Firstpage :
1
Lastpage :
6
Abstract :
Mining data streams for mobile and embedded applications faces a major problem represented in the high rate of the streaming input with regard to the available computational resources. Adapting the data mining algorithms to the availability of resources is an essential step towards realizing the potential applications in this area. In this paper, we review our Algorithm Output Granularity (AOG) for data stream mining adaptation. The generalization of AOG based on Probably Approximately Correct (PAC) learning model is presented. This generalization is of paramount importance to establish a theoretical framework for adaptation and resource-awareness in data stream mining.
Keywords :
adaptive systems; data mining; embedded systems; medical information systems; mobile computing; adaptation; adaptive data stream mining; algorithm output granularity; embedded application; mobile application; probably approximately correct learning model; resource awareness; Application software; Availability; Change detection algorithms; Clustering algorithms; Data mining; Electronic mail; Machine learning; Machine learning algorithms; Mobile computing; Software engineering; Algorithm Output Granularity; Data Stream Mining;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Biomedical Engineering Conference, 2008. CIBEC 2008. Cairo International
Conference_Location :
Cairo
Print_ISBN :
978-1-4244-2694-2
Electronic_ISBN :
978-1-4244-2695-9
Type :
conf
DOI :
10.1109/CIBEC.2008.4786099
Filename :
4786099
Link To Document :
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