Classification and Data Mining by Bruno Bertaccini, Roberta Varriale (auth.), Antonio Giusti,

By Bruno Bertaccini, Roberta Varriale (auth.), Antonio Giusti, Gunter Ritter, Maurizio Vichi (eds.)

​​​​​​​​​This quantity includes either methodological papers exhibiting new unique equipment, and papers on functions illustrating how new domain-specific wisdom could be made to be had from info via shrewdpermanent use of information research tools. the amount is subdivided in 3 components: class and information research; info Mining; and functions. the choice of peer reviewed papers were offered at a gathering of category societies held in Florence, Italy, within the sector of "Classification and knowledge Mining".​

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Applications on the perception of the value or of the quality of objects are common in various fields: teaching evaluation, health system or public services, risk analysis, university services performances, measurement system analysis and many others. One of the innovative tools in the evaluation analysis area assumes that the ordinal L. Deldossi ( ) R. it A. Giusti et al. 1007/978-3-642-28894-4 3, © Springer-Verlag Berlin Heidelberg 2013 19 20 L. Deldossi and R. Paroli results can be thought as the final outcome of an unobserved choice mechanism with two latent components: the feeling with the items or the objects, which is a latent continuous random variable discretized by the judgment or the rate, and the uncertainty in the choice of rates, which is related to several individual factors such as knowledge or ignorance of the problem, personal interests, opinions, time spent in the decision and so on.

2004). General conditions for predictivity in learning theory. Nature, 428, 419–422. , & Smola, A. J. (2002). Learning with kernels. Cambridge: MIT. Steinwart, I. (2002). Support vector machines are universally consistent. Journal of Complexity, 18, 768–791. , & Christmann, A. (2008). Support vector machines. New York: Springer. Vapnik, V. N. (1998). Statistical learning theory. New York: Wiley. Issues on Clustering and Data Gridding Jukka Heikkonen, Domenico Perrotta, Marco Riani, and Francesca Torti Abstract This contribution addresses clustering issues in presence of densely populated data points with high degree of overlapping.

Two different but not independent aspects have to be considered in the analysis: the former can be suitably faced by factorial techniques; the latter, the comparison among different solutions obtained for each data batch, remains the main issue in the analysis. A straightforward approach for a one-to-one comparison is to perform a supplementary projection of a data batch on the factorial structure resulting from a previous data batch; a slightly more sophisticated approach is to update the obtained solution progressively when new data batches comes in (Iodice D’Enza and Greenacre, 2010).

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