By Yanglv Ling
These lawsuits of the 2014 Pacific-Asia Workshop on Computational Intelligence in commercial program (CIIA 2014) contain eighty one peer-reviewed papers. the subjects lined within the ebook comprise: (1) computing device Intelligence, (2) software of computing device technological know-how and communique, (3) business Engineering, Product layout and production, (4) Automation and regulate, info expertise and MEMS.
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In Data Mining, 2007. ICDM 2007. Seventh IEEE International Conference on, pp. 739–744. IEEE. , R. Ehmke, M. Mennes, D. Imperati, F. X. Castellanos, O. Sporns, & M. P. Milham (2012). Network centrality in the human functional connectome. Cerebral Cortex 22(8), 1862–1875. ) © 2015 Taylor & Francis Group, London, ISBN: 978-1-138-02818-0 Research on security defense model of oil & gas gathering and transferring SCADA based on factor neural network P. D. Cao, Y. W. H. Hu Electrical & Information School, Southwest Petroleum University, Chengdu, Sichuan, China ABSTRACT: This paper applies the method of framework and rules combined to describe the analytical factors neuron and proposes the security defense model of oil & gas gathering and transferring SCADA based on factor neural network.
Hama platform is based on the implementation MapReduce and BSP (Bulk Synchronous Parallel). It uses the transportation of messages in BSP to accelerate the speed of the iterative process, and doesn’t need multiple MapReduce jobs, the cost of HDFS persistence and data serialization stay unchanged. So we, in this paper, design and implement the classical data algorithm K-Means in Hama platform, to pave the way for further complex data mining algorithm. KEYWORDS: Hadoop; MapReduce; BSP; Hama; K-means.
4 Set the BSP Job object and set the parameters for distributed tasks up. 2 Parallel K-Means algorithm on a single Hadoop platform Below shows K-Means algorithm in parallel on Hama Since calculating the distance between data object with the initial cluster centers and calculate new cluster centers are independent from each other, so it can be achieved in MapReduce model. Algorithm process is as follows: Algorithm 3: Parallel algorithm on Hama Input: k: Number of clusters D: Dataset containing n objects Algorithm 2: K-Means on Hadoop Output: k clustered groups Method: 1.