Reducing Communication and Merging Overheads for Distributed Clustering Algorithms on the Cloud

Chun Chieh Chen, Tze Yu Chen, Jen Wei Huang, Ming Syan Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Many distributed clustering algorithms have been proposed to speed up data clustering on huge database. However, the existing distributed clustering algorithms still suffer from many issues on distributed system such as data synchronization, insufficient scalability, and maintenance difficulties. In this paper, we propose two distributed clustering algorithms named DDC and DGC, which are based on the cloud computing technique. The main ideas of proposed algorithms are to achieve load balance according to an efficient data partition, to cluster more data on many machines in parallel without data dependency, and to merge the result on a machine efficiently with minimal information overlap. The experimental results show that DDC and DGC are able to reduce the execution time and achieve great scalability on the cloud.

Original languageEnglish
Title of host publicationProceedings - 2015 International Conference on Cloud Computing and Big Data, CCBD 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages41-48
Number of pages8
ISBN (Electronic)9781467383509
DOIs
Publication statusPublished - 2016 Apr 8
EventInternational Conference on Cloud Computing and Big Data, CCBD 2015 - Shanghai, China
Duration: 2015 Nov 42015 Nov 6

Publication series

NameProceedings - 2015 International Conference on Cloud Computing and Big Data, CCBD 2015

Other

OtherInternational Conference on Cloud Computing and Big Data, CCBD 2015
Country/TerritoryChina
CityShanghai
Period15-11-0415-11-06

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems

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