Advances in Knowledge Discovery and Management: Volume 4 by François Queyroi (auth.), Fabrice Guillet, Bruno Pinaud,

By François Queyroi (auth.), Fabrice Guillet, Bruno Pinaud, Gilles Venturini, Djamel Abdelkader Zighed (eds.)

This ebook is a set of consultant and novel works performed in info Mining, wisdom Discovery, Clustering and category that have been initially offered in French on the EGC'2012 convention held in Bordeaux, France, on January 2012. This convention was once the twelfth variation of this occasion, which occurs every year and that's now winning and recognized within the French-speaking neighborhood. This neighborhood used to be dependent in 2003 through the basis of the French-speaking EGC society (EGC in French stands for ``Extraction et Gestion des Connaissances'' and skill ``Knowledge Discovery and Management'', or KDM).

This booklet is meant to be learn through all researchers attracted to those fields, together with PhD or MSc scholars, and researchers from public or inner most laboratories. It issues either theoretical and functional features of KDM. The publication is established in components referred to as ``Knowledge Discovery and information Mining'' and ``Classification and have Extraction or Selection''. the 1st half (6 chapters) offers with information clustering and knowledge mining. the 3 last chapters of the second one half are with regards to class and have extraction or function selection.

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As for the other ones with intermediate average power consumption, they do not show any correlation with the period of the day and thus do not allow an immediate interpretation. Nonparametric Hierarchical Clustering of Functional Data January 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 February 22 23 24 25 26 27 28 29 30 31 1 2 3 4 5 6 7 8 9 10 11 July 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 12 13 14 15 16 17 18 19 20 21 22 23 24 25 March 26 27 28 1 2 3 4 5 6 7 8 9 10 11 August 23 24 25 26 27 28 29 30 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 12 13 14 15 16 17 18 April 19 20 21 22 23 24 25 26 27 28 29 30 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 September 27 28 29 30 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 33 24 25 26 27 28 29 30 16 17 18 19 20 21 22 May 23 24 25 26 27 28 29 30 1 2 3 4 5 6 7 8 9 10 11 12 13 October 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 14 15 16 17 18 19 20 June 21 22 23 24 25 26 27 28 29 30 31 1 2 3 4 5 6 7 8 9 10 November 29 30 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 1 December 26 27 28 29 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Fig.

Each curve is sampled at mi values in [a, b], leading to a i , with yi j = ci (xi j ). series of observations denoted ci = (xi j , yi j )mj=1 As in all data exploratory settings, our main goal is to reduce the complexity of the data set and to discover patterns in the data. We are therefore interested in finding clusters of similar functions as well as in finding functional patterns, that is systematic and simple regular shapes in individual functions. , 2010]. , 2003] but with no simplification virtues.

Both approaches are for that matter compared in this article. The method has no parameters and obtains in a fully automated way an optimal summary of the functional data set, using a Bayesian approach with data dependent priors. In some cases, especially for large scale data sets, the optimal number of clusters and of sub-intervals may be too large for a user to interpret all the discovered fine grained patterns in a reasonable time. Therefore, the method is complemented with a post-processing step which offers the user a way to decrease the number of clusters in a greedy optimal way.

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