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Performance evaluation of a distributed clustering approach for spatial datasets

Bendechache, Malika orcid logoORCID: 0000-0003-0069-1860, Le-Khac, Nhien-An and Kechadi, M-Tahar orcid logoORCID: 0000-0002-0176-6281 (2018) Performance evaluation of a distributed clustering approach for spatial datasets. In: Australasian Conference on Data Mining AusDM 2017, 19-22 Aug 2017, Melbourne, VIC, Australia. ISBN 978-981-13-0291-6

The analysis of big data requires powerful, scalable, and accurate data analytics techniques that the traditional data mining and machine learning do not have as a whole. Therefore, new data analytics frameworks are needed to deal with the big data challenges such as volumes, velocity, veracity, variety of the data. Distributed data mining constitutes a promising approach for big data sets, as they are usually produced in distributed locations, and processing them on their local sites will reduce significantly the response times, communications, etc. In this paper, we propose to study the performance of a distributed clustering, called Dynamic Distributed Clustering (DDC). DDC has the ability to remotely generate clusters and then aggregate them using an efficient aggregation algorithm. The technique is developed for spatial datasets. We evaluated the DDC using two types of communications (synchronous and asynchronous), and tested using various load distributions. The experimental results show that the approach has super-linear speed-up, scales up very well, and can take advantage of the recent programming models, such as MapReduce model, as its results are not affected by the types of communications
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Uncontrolled Keywords:Distributed data mining; distributed computing; synchronous communication; asynchronous communication; spacial data mining; superspeedup
Subjects:Computer Science > Algorithms
Computer Science > Machine learning
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > INSIGHT Centre for Data Analytics
Published in: 15th Australasian Conference, AusDM 2017. Communications in Computer and Information Science 854. Springer. ISBN 978-981-13-0291-6
Official URL:http://dx.doi.org/10.1007/978-981-13-0292-3_3
Copyright Information:© 2018 Springer
Funders:Science Foundation Ireland under Grant Number SFI/12/RC/2289.
ID Code:24628
Deposited On:17 Jun 2020 10:11 by Malika Bendechache . Last Modified 17 Jun 2020 10:11

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