By Fabrice Guillet, Bruno Pinaud, Gilles Venturini
This publication offers a set of consultant and novel paintings within the box of information mining, wisdom discovery, clustering and type, in response to increased and transformed types 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 e-book is in 3 components: the 1st 4 chapters speak about optimization issues in info mining. the second one half explores particular caliber measures, dissimilarities and ultrametrics. the ultimate chapters specialise 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 points of information discovery and management.
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Extra resources for Advances in Knowledge Discovery and Management: Volume 6
The user may select points that are not necessarily optimal but that represent good alternatives to the regular skyline answers (in the case, for instance, where the latter look “too good to be true”). Finally, an element of the graded skyline is associated with two scores: a degree of membership to the skyline, and a typicality degree (that expresses the extent to which it is not exceptional). One may imagine different ways of navigating inside areas in order to explore the set of answers: a simple scan for displaying the characteristics of the points, the use of different filters aimed, for instance, at optimizing diversity on certain attributes, etc.
Given a set of points in a space, a skyline query retrieves those points that are not dominated by any other in the sense of Pareto order. When the number of dimensions on which preferences are expressed gets high, many tuples may become incomparable. Several approaches have been proposed to define an order for two incomparable tuples, based on the number of other tuples that each of the two tuples dominates (notion of k-representative dominance proposed in Lin et al. (2007)), on a preference order over the attributes (see for instance the notions of k-dominance and k-frequency introduced in Chan et al.
Sahami, M. (1996). Toward optimal feature selection. In International Conference on Machine Learning (pp. 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.
Advances in Knowledge Discovery and Management: Volume 6 by Fabrice Guillet, Bruno Pinaud, Gilles Venturini