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 e-book is a set of consultant and novel works performed in information Mining, wisdom Discovery, Clustering and class that have been initially awarded 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 profitable and recognized within the French-speaking neighborhood. This neighborhood used to be established in 2003 by way of the root of the French-speaking EGC society (EGC in French stands for ``Extraction et Gestion des Connaissances'' and capability ``Knowledge Discovery and Management'', or KDM).

This e-book is meant to be learn by means of all researchers drawn to those fields, together with PhD or MSc scholars, and researchers from public or inner most laboratories. It matters either theoretical and useful elements of KDM. The publication is based in elements referred to as ``Knowledge Discovery and information Mining'' and ``Classification and have Extraction or Selection''. the 1st half (6 chapters) bargains with information clustering and information mining. the 3 ultimate chapters of the second one half are regarding class and have extraction or characteristic selection.

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Comparative analysis of the modeling results. , 2010], we propose to study a simplified data grid by coarsening the optimal model until having four clusters, using the post-processing technique detailed in Section 4. By doing this, 50% of the information is kept and the power consumption and the time discretizations are reduced to four intervals. Contrary to MODL, the approach Nonparametric Hierarchical Clustering of Functional Data (a) Dendrogram 29 (b) Pareto chart Fig. , 2010] requires the user to specify the number of clusters and time segments.

In this type of approach, both the number of prototypes and the number of segments (constant parts of the prototypes) are under user control. On a positive side, this limits the risk of cognitive overwhelming as the user can ask for a low complexity representation. , 2010], increasing the risk of over/under-fitting. g. , 2010], [Gaffney and Smyth, 2004], [Ramsay and Silverman, 2005]. All those methods make (sometimes implicit) assumptions on the distribution of the functions and/or on the measurement noise.

10 Calendar of the year 2007 retrieved using MODL. Each line represents a day of the week. There are four colors (one per cluster), the redder the color, the higher the average power consumption of the cluster is. The white days correspond to days with missing data. However, the cluster 4 is different in that the density peak has been translated to an upper power interval. Finally, the cluster 2 highlights multimodalities with three power values around which the measurements are dense. This complex pattern has been retrieved by MODL since it based on joint density estimation; the competing approach cannot track such patterns.

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