By Fabrice Guillet, Bruno Pinaud, Gilles Venturini
This ebook provides a set of consultant and novel paintings within the box of information mining, wisdom discovery, clustering and type, in line with improved and remodeled 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 components: the 1st 4 chapters speak about optimization concerns in facts mining. the second one half explores particular caliber measures, dissimilarities and ultrametrics. the ultimate chapters concentrate on semantics, ontologies and social networks.
Written for PhD and MSc scholars, in addition to researchers operating within the box, it addresses either theoretical and sensible elements of data discovery and management.
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This booklet offers a suite of consultant and novel paintings within the box of knowledge mining, wisdom discovery, clustering and type, in line with multiplied and transformed models of a variety of the simplest papers initially awarded in French on the EGC 2014 and EGC 2015 meetings held in Rennes (France) in January 2014 and Luxembourg in January 2015.
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Extra resources for Advances in Knowledge Discovery and Management: Volume 6
Hue et al. 5), the second one enables the most important reduction of the number of kept variables. As far as the weights sum is concerned, all the p regularization exponents enable to reduce the weights sum on average. Moreover, given a λ weight, the quadratic regularization has a less important impact on the weights sum reduction than the two other regularizations whose performance are very close for this indicator. 1 seems the most favorable. Without deteriorating the performance of the non regularized classifier, it enables a significant reduction of the number of selected variables.
All the algorithms can be implemented this way. However, to obtain maximum speed, some computations have to be implemented more efficiently. With Thrust, each reduce operation brings back the result into the CPU memory. In some cases, it would be better to keep it in the GPU memory. ) 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.
Zadeh, L. A. (1984). A computational theory of dispositions. In Y. ), COLING (pp. 312–318). ACL. Zadeh, L. (1987). A computational theory of dispositions. International Journal of Intelligent Systems, 2, 39–63. Zhang, J. (2013). Advancements of outlier detection: A survey. EAI Endorsed Transactions on Scalable Information Systems, 1, e2. , Campello, R. J. G. , & Sander, J. (2013). Subsampling for efficient and effective unsupervised outlier detection ensembles. In The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, Chicago, IL, USA, 11–14 August 2013 (pp.
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