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Parallel computing TEDA for high frequency streaming data clustering

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Publication date23/10/2016
Host publicationAdvances in Big Data: Proceedings of the 2nd INNS Conference on Big Data, October 23-25, 2016, Thessaloniki, Greece
EditorsPlamen Angelov, Yannis Manolopoulos, Lazaros Iliadis, Asim Roy, Marley Vellasco
Place of PublicationCham
PublisherSpringer
Pages238-253
Number of pages16
ISBN (Electronic)9783319478982
ISBN (Print)9783319478975
Original languageEnglish
Event2nd International Neural Network Society Conference on Big Data, INNS 2016 - Thessaloniki, Greece
Duration: 23/10/201625/10/2016

Conference

Conference2nd International Neural Network Society Conference on Big Data, INNS 2016
CountryGreece
CityThessaloniki
Period23/10/1625/10/16

Conference

Conference2nd International Neural Network Society Conference on Big Data, INNS 2016
CountryGreece
CityThessaloniki
Period23/10/1625/10/16

Abstract

In this paper, a novel online clustering approach called Parallel_TEDA is introduced for processing high frequency streaming data. This newly proposed approach is developed within the recently introduced TEDA theory and inherits all advantages from it. In the proposed approach, a number of data stream processors are involved, which collaborate with each other efficiently to achieve parallel computation as well as a much higher processing speed. A fusion center is involved to gather the key information from the processors which work on chunks of the whole data stream and generate the overall output. The quality of the generated clusters is being monitored within the data processors all the time and stale clusters are being removed to ensure the correctness and timeliness of the overall clustering results. This, in turn, gives the proposed approach a stronger ability of handling shifts/drifts that may take place in live data streams. The numerical experiments performed with the proposed new approach Parallel_TEDA on benchmark datasets present higher performance and faster processing speed when compared with the alternative well-known approaches. The processing speed has been demonstrated to fall exponentially with more data processors involved. This new online clustering approach is very suitable and promising for real-time high frequency streaming processing and data analytics.