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On the Accuracy and Parallelism of GPGPU-Powered Incremental Clustering Algorithms
Joint Authors
Chen, Chunlei
He, Li
Zhang, Huixiang
Zheng, Hao
Wang, Lei
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-10-11
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Incremental clustering algorithms play a vital role in various applications such as massive data analysis and real-time data processing.
Typical application scenarios of incremental clustering raise high demand on computing power of the hardware platform.
Parallel computing is a common solution to meet this demand.
Moreover, General Purpose Graphic Processing Unit (GPGPU) is a promising parallel computing device.
Nevertheless, the incremental clustering algorithm is facing a dilemma between clustering accuracy and parallelism when they are powered by GPGPU.
We formally analyzed the cause of this dilemma.
First, we formalized concepts relevant to incremental clustering like evolving granularity.
Second, we formally proved two theorems.
The first theorem proves the relation between clustering accuracy and evolving granularity.
Additionally, this theorem analyzes the upper and lower bounds of different-to-same mis-affiliation.
Fewer occurrences of such mis-affiliation mean higher accuracy.
The second theorem reveals the relation between parallelism and evolving granularity.
Smaller work-depth means superior parallelism.
Through the proofs, we conclude that accuracy of an incremental clustering algorithm is negatively related to evolving granularity while parallelism is positively related to the granularity.
Thus the contradictory relations cause the dilemma.
Finally, we validated the relations through a demo algorithm.
Experiment results verified theoretical conclusions.
American Psychological Association (APA)
Chen, Chunlei& He, Li& Zhang, Huixiang& Zheng, Hao& Wang, Lei. 2017. On the Accuracy and Parallelism of GPGPU-Powered Incremental Clustering Algorithms. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1139852
Modern Language Association (MLA)
Chen, Chunlei…[et al.]. On the Accuracy and Parallelism of GPGPU-Powered Incremental Clustering Algorithms. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-12.
https://search.emarefa.net/detail/BIM-1139852
American Medical Association (AMA)
Chen, Chunlei& He, Li& Zhang, Huixiang& Zheng, Hao& Wang, Lei. On the Accuracy and Parallelism of GPGPU-Powered Incremental Clustering Algorithms. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1139852
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1139852