Algorithms and Architectures for Parallel Processing: 14th by Xian-he Sun, Wenyu Qu, Ivan Stojmenovic, Wanlei Zhou,

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 lawsuits of the 14th foreign convention on Algorithms and Architectures for Parallel Processing, ICA3PP 2014, held in Dalian, China, in August 2014. The 70 revised papers awarded within the volumes have been chosen from 285 submissions. the 1st quantity contains chosen papers of the most convention and papers of the first foreign 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 foreign 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, communique and keep watch over applied sciences in clever Transportation procedure, 3C in ITS 2014, and the Workshop on protection and privateness in computing device and community platforms, SPCNS 2014.

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Extra info for Algorithms and Architectures for Parallel Processing: 14th International Conference, ICA3PP 2014, Dalian, China, August 24-27, 2014. Proceedings, Part II

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Xu et al. is one of the most widely used clustering methods, but it suffers from the wellknown problem that converges to a local optimum. Due to the reason that it is highly dependent upon the chosen of initial centers. In recent years, many researches have focused on improving its initialization method [5,6]. An important piece of work in this direction is the k-means++ [7]. This algorithm is fast with small data in practice. Moreover, it obtains an O(logk) approximation solution to the optimal result of k-means and gives a theoretical guarantee firstly.

Performance test results show that the new model is not only able to achieve a higher throughput and efficiency, but also able to achieve the “green computing” goal. Companies and schools can leverage existing idle desktop PC resources running MapReduce job for massive 14 B. Tang, H. He, and G. Fedak data analysis, and the proposed method also reduces the computational cost overhead, which has a great potential. Acknowledgments. This work is supported by the French Agence Nationale de la Recherche through the MapReduce grant under contract ANR-10-SEGI001-01, as well as INRIA ARC BitDew.

Finally, the algorithm updates φX (C) and the iteration continues. Since the number of chosen points is more than k (the expected number of points in C is ∗ O(logψ)) after the O(logψ) iterations, it uses a weighted k-means++ to obtain the final k centers. Algorithm 1. Scalable k-means++ Initialization Input : k, the number of clusters. X = {x1 , x2 , . . , xn }, a set of data points. , ck }. 1 C ← sample a point uniformly at random from X 2 ψ ← φX (C) 3 for O(logψ) times do 4 C sample each point x ∈ X independently with probability px = 5 6 7 3 d2 (x,C) φX (C) C ← C ∪ C , compute φX (C) For x ∈ C, set wx to be the number of points in X closer to x than any other point in C Recluster the weighted points in C into k clusters Our Method In this section, we first introduce the Parallel Scalable k-means++ with MapReduce (PSKM++), and then present our improved version of PSKM++, Oversampling and Refining (OnR).

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