By Xian-he Sun, Wenyu Qu, Ivan Stojmenovic, Wanlei Zhou, Zhiyang Li, Hua Guo, Geyong Min, Tingting Yang, Yulei Wu, Lei Liu (eds.)
This quantity set LNCS 8630 and 8631 constitutes the court cases of the 14th overseas convention on Algorithms and Architectures for Parallel Processing, ICA3PP 2014, held in Dalian, China, in August 2014. The 70 revised papers provided within the volumes have been chosen from 285 submissions. the 1st quantity contains chosen papers of the most convention and papers of the first overseas Workshop on rising issues in instant and cellular Computing, ETWMC 2014, the fifth overseas Workshop on clever verbal exchange Networks, IntelNet 2014, and the fifth overseas Workshop on instant Networks and Multimedia, WNM 2014. the second one quantity includes chosen papers of the most convention and papers of the Workshop on Computing, conversation and regulate applied sciences in clever Transportation method, 3C in ITS 2014, and the Workshop on safeguard and privateness in desktop and community platforms, SPCNS 2014.
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Additional info for Algorithms and Architectures for Parallel Processing: 14th International Conference, ICA3PP 2014, Dalian, China, August 24-27, 2014. Proceedings, Part II
IEEE Computer Society (2012) 12. : Towards mapreduce for desktop grid computing. , Aleksy, M. ) 3PGCIC, pp. 193–200. IEEE Computer Society (2010) 13. : Improving mapreduce performance in heterogeneous environments. , van Renesse, R. ) OSDI, pp. 29–42. com Abstract. K-means++ is undoubtedly one of the most important initializing algorithms for k-means owing to its provable approximation guarantee to the optimal solution. However, due to its sequential nature, k-means++ requires a large number of iterations to complete the initialization and it becomes ineﬃcient as the size of data increase.
Finally, Section 5 concludes the paper. 2 Preliminaries To provide a technical context for the discussion in this paper, we begin with preliminaries. First, we give the deﬁnition of clustering cost. Then, we describe the scalable k-means++ algorithm in more detail. , ck } divides X into k exhaustive clusters and the following function is the clustering cost. min ||x − c||2 φX (C) = x∈X c∈C Scalable k-means++  modiﬁes the initialization setup of k-means++  and obtains an eﬃcient parallel version.
104–111 (1988) 10. : P2P-Mapreduce: Parallel data processing in dynamic cloud environments. J. Comput. Syst. Sci. 78(5), 1382–1402 (2012) 11. : Analysis of data reliability tradeoﬀs in hybrid distributed storage systems. In: IPDPS Workshops, pp. 1546–1555. IEEE Computer Society (2012) 12. : Towards mapreduce for desktop grid computing. , Aleksy, M. ) 3PGCIC, pp. 193–200. IEEE Computer Society (2010) 13. : Improving mapreduce performance in heterogeneous environments. , van Renesse, R. ) OSDI, pp.
Algorithms and Architectures for Parallel Processing: 14th International Conference, ICA3PP 2014, Dalian, China, August 24-27, 2014. Proceedings, Part II by Xian-he Sun, Wenyu Qu, Ivan Stojmenovic, Wanlei Zhou, Zhiyang Li, Hua Guo, Geyong Min, Tingting Yang, Yulei Wu, Lei Liu (eds.)