Download PDF by Fabrice Guillet, Bruno Pinaud, Gilles Venturini: Advances in Knowledge Discovery and Management: Volume 6

By Fabrice Guillet, Bruno Pinaud, Gilles Venturini

ISBN-10: 3319457624

ISBN-13: 9783319457628

ISBN-10: 3319457632

ISBN-13: 9783319457635

This publication offers a set of consultant and novel paintings within the box of knowledge mining, wisdom discovery, clustering and class, in accordance with accelerated and transformed models of a variety of the easiest papers initially offered in French on the EGC 2014 and EGC 2015 meetings held in Rennes (France) in January 2014 and Luxembourg in January 2015. The ebook is in 3 elements: the 1st 4 chapters speak about optimization concerns in info mining. the second one half explores particular caliber measures, dissimilarities and ultrametrics. the ultimate chapters specialize in semantics, ontologies and social networks.
Written for PhD and MSc scholars, in addition to researchers operating within the box, it addresses either theoretical and useful elements of data discovery and management.

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Causes many data transfers between CPU and GPU memories. )) in Algorithm 5 is not fast enough with Thrust because of the data transfers between the GPU and the CPU when implemented with Thrust. Such memory transfers are very slow. It is necessary to rewrite some parts of the program without Thrust. Firstly, it appears very important to organize data in the global memory of the GPU in a way that memory accesses are coalesced between threads. In CUDA, threads are grouped by warps of 32, to work together.

284–292). Kuncheva, L. , & Rodríguez, J. J. (2007). Classifier ensembles with a random linear oracle. IEEE Transactions on Knowledge and Data Engineering, 19(4), 500–508. Lange, K. (2004). Optimization. Springer Texts in Statistics. New York: Springer. , & Thompson, K. (1992). An analysis of bayesian classifiers. In National Conference on Artificial Intelligence (pp. 223–228). Nesterov, Y. (2004). Introductory lectures on convex optimization: A basic course. Applied optimization. Boston: Kluwer Academic Publishers.

6 CUDA compared to sequential algorithm 1e+06 1e+07 36 H. Jaudoin et al. 400 100k tuples 200k tuples 500k tuples Computation Time (s) 350 300 250 200 150 100 50 0 0 5 10 15 20 25 30 35 40 Attribute number Fig. 7 Impact of the number of attributes These results show that it is possible to calculate a graded Skyline within a reasonable time, even on a large number of tuples with many attributes. 6 Conclusion In this paper, we have proposed a graded version of skyline queries aimed at controlling the impact of exceptions on the result (so as to prevent interesting points to be hidden because they are dominated by exceptional ones).

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Advances in Knowledge Discovery and Management: Volume 6 by Fabrice Guillet, Bruno Pinaud, Gilles Venturini


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