Download e-book for kindle: New Developments in Classification and Data Analysis: by Massimo Aria (auth.), H.-H. Bock, W. Gaul, M. Vichi, Ph.

By Massimo Aria (auth.), H.-H. Bock, W. Gaul, M. Vichi, Ph. Arabie, D. Baier, F. Critchley, R. Decker, E. Diday, M. Greenacre, C. Lauro, J. Meulman, P. Monari, S. Nishisato, N. Ohsumi, O. Opitz, G. Ritter, M. Schader, C. Weihs, Professor Maurizio Vichi, Prof

ISBN-10: 3540238093

ISBN-13: 9783540238096

ISBN-10: 3540273735

ISBN-13: 9783540273738

This quantity comprises revised models of chosen papers provided throughout the biannual assembly of the type and knowledge research workforce of SocietA Italiana di Statistica, which was once held in Bologna, September 22-24, 2003. The medical application of the convention incorporated eighty contributed papers. furthermore it was once attainable to recruit six across the world popular invited spe- ers for plenary talks on their present examine works in regards to the center subject matters of IFCS (the overseas Federation of category Societies) and Wo- gang Gaul and the colleagues of the GfKl geared up a consultation. therefore, the convention supplied loads of scientists and specialists from domestic and out of the country with an enticing discussion board for discussions and mutual trade of information. The talks within the diverse classes concerned about methodological advancements in supervised and unsupervised type and in facts research, additionally p- viding correct contributions within the context of functions. This instructed the presentation of the forty three chosen papers in 3 elements as follows: class AND CLUSTERING Non parametric type Clustering and dissimilarities MULTIVARIATE information and knowledge research utilized MULTIVARIATE data Environmental facts Microarray information Behavioural and textual content facts monetary information we want to exhibit our gratitude to the authors whose enthusiastic p- ticipation made the assembly attainable. we're very thankful to the reviewers for the time spent of their expert reviewing paintings. we'd additionally wish to expand our because of the chairpersons and discussants of the classes: their reviews and recommendations proved very stimulating either for the authors and the audience.

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Additional resources for New Developments in Classification and Data Analysis: Proceedings of the Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society, University of Bologna, September 22–24, 2003

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Journal of the American Statistical Association, 457, 77-87. , TAMAYO, P. et al. (1999): Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring. Science, 286, 531-537. HWARINEN, A,, KARHUNEN, J . and OJA, E. (2001): Independent Component Analysis, Wiley, New York. , RINGNER, M. et al. (2001): Classification and Diagnostic Prediction of Cancers Using Gene Expression Profiling and Artificial Neural Networks. Nature Medicine, 7, 673-679. , NARASIMHAN, B.

As shown in Table 3, none of the three methods is successful in accurately predict the class membership. 042). It is worth noting that these minimum values are referred to approximately the same number of selected genes. 333 Table 3. Leukemia data set: cross-validated misclassification rates for different values of m ( k = 7 for ICA and k = 5 for SVD). 5 Conclusions and open issues As the preliminary results on these real data sets show, the proposed strategy seems to represent a useful tool to detect subsets of relevant genes for supervised cell classification based on microarray data.

The proposed solution consists of building classification rules on genes selected by looking at the tails of the distributions of gene projections along suitable directions. Since gene expression profiles are typically non-gaussian, it seems relevant to catch not only the linear (second-order) aspects of the data structure but also the non-linear (higher-order) ones. For this reason, our proposal focuses on searching the less statistically dependent projections. These directions are obtained by independent component analysis (Hyvarinen et al.

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New Developments in Classification and Data Analysis: Proceedings of the Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society, University of Bologna, September 22–24, 2003 by Massimo Aria (auth.), H.-H. Bock, W. Gaul, M. Vichi, Ph. Arabie, D. Baier, F. Critchley, R. Decker, E. Diday, M. Greenacre, C. Lauro, J. Meulman, P. Monari, S. Nishisato, N. Ohsumi, O. Opitz, G. Ritter, M. Schader, C. Weihs, Professor Maurizio Vichi, Prof


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