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Efficient change detection for high dimensional data streams

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Autor
Georgakopoulos S.V., Tasoulis S.K., Plagianakos V.P.
Datum
2015
Language
en
DOI
10.1109/BigData.2015.7364010
Schlagwort
Algorithms
Clustering algorithms
Data communication systems
Data handling
Data mining
Mobile devices
Principal component analysis
Signal detection
Cumulative sums
Data stream
Dimensionality reduction
High dimensional data
High-dimensional data streams
Incremental principal component analysis
Processing functionality
Technological advancement
Big data
Institute of Electrical and Electronics Engineers Inc.
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Zusammenfassung
The recent technological advancements in cloud computing and the access in increasing computational power has led in undertaking the data processing derived by mobile devices. In particular, when these data are high dimensional this is indispensable, since the mobile device has to balance its processing functionalities to additional services. However, developing efficient algorithms could allow various types of analysis to be performed locally, avoiding the necessity of a constantly connected device. In this work, we present a methodology that combines lightweight dimensionality reduction and change detection techniques. The experimental results justify its impressive performance and subsequently its usefulness in several tasks. © 2015 IEEE.
URI
http://hdl.handle.net/11615/72071
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