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

By Fabrice Guillet, Bruno Pinaud, Gilles Venturini

This ebook offers a set of consultant and novel paintings within the box of information mining, wisdom discovery, clustering and type, in line with elevated and transformed models of a range of the simplest 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 booklet is in 3 elements: the 1st 4 chapters speak about optimization issues in facts 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 sensible facets of data discovery and management.

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By definition, the skyline points are on the border of the region that includes the points of the dataset. However, these points are very distant from the areas corresponding to the two groups and are thus not very representative of the dataset. It could then be interesting for a user to be able to visualize the points that are “almost dominant”, closer to the clusters, then more representative of the dataset. A way to make such points visible without discarding extrema, while allowing to discriminate them, is to use a gradual view of representativity.

Other optimizations can be thought of Harris (2007) shows how to efficiently design reduce functions on CUDA. It is better to group map and reduce operations when the latter is applied on the former. com/cuda/thrust/. On Making Skyline Queries Resistant to Outliers 33 of reduce. To avoid waiting cycles in alternatives, it is worth to replace all branches by simple computations, when possible. 5 Experimental Results We have experimented our implementation on both synthetic and real-world datasets.

Then, for map, the kernel is launched with as many instances as there are data to process. So, in theory, map( f, C) has the same order of complexity than function f (multiplied by the collection size, divided by the number of parallel threads). For reduce, the kernel is launched hierarchically following a binary tree scheme. If f has a time complexity of θ (1), then r educe( f, C) has a complexity of θ (log2 n) where n is the cardinality of C. Using these principles, we propose Algorithm 5 that computes the degree of membership to the skyline of every tuple in the dataset.

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