Download Advances in Data Analysis: Proceedings of the 30th Annual by Gilles Celeux (auth.), Professor Dr. Reinhold Decker, PDF

By Gilles Celeux (auth.), Professor Dr. Reinhold Decker, Professor Dr. Hans -J. Lenz (eds.)

This ebook makes a speciality of exploratory info research, studying of latent buildings in datasets, and unscrambling of information. It covers a wide diversity of tools from multivariate statistics, clustering and category, visualization and scaling in addition to from info and time sequence research. It presents new methods for info retrieval and information mining. moreover, the e-book stories not easy functions in advertising and marketing and administration technological know-how, banking and finance, bio- and healthiness sciences, linguistics and textual content research, statistical musicology and sound category, in addition to archaeology. detailed emphasis is wear interdisciplinary study and the interplay among conception and practice.

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Extra info for Advances in Data Analysis: Proceedings of the 30th Annual Conference of the Gesellschaft fur Klassifikation e.V., Freie Universitat Berlin, March 8–10, 2006

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1993): An Empirical Pooling Approach for Estimating Marketing Mix Elasticities with PIMS Data. Marketing Science, 12, 103–124. SCHWARZ, G. (1978): Estimating the Dimension of a Model. Annals of Statistics, 6, 461–464. S. (1938): The Large Sample Distribution of the Likelihood Ratio for Testing Composite Hypotheses. Annals of Mathematical Statistics, 9, 60–62. H. (1970): Pattern Clustering by Multivariate Mixture Analysis. Multivariate Behavioral Research, 5, 329-350. pl Abstract. The paper presents difficulties of measuring clustering quality for symbolic data (such as lack of a ”traditional” data matrix).

References CRAMER, H. (1964): Stochastic Processes as Curves on Hilbert Space. Theory Probab. Appl. , FRIDLYAND, J. and SPEED, T. (2002): Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data. Journal of the American Statistical Association, 97, 77–87. FRIDLYAND, J. and DUDOIT, S. (2002): A Prediction-based Resampling Method to Estimate the Number of Clusters in a Dataset. Genome Biology, 3, 7. MEILA, M. and SHI, J. (2001): A Random Walks View of Spectral Segmentation.

J. (1990): Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York. J. T. (1988): A Criterion for Determining the Number of Groups in a Data Set Using Sum of Squares Clustering. Biometrics, 44, 23-34. H. (1971). Practical Problems in a Method of Cluster Analysis. Biometrics, 27, 501-514. L. (1966): Similarity Analysis by Reciprocal Pairs for Discrete and Continuous Data. Educational and Psychological Measurement, 26, 825-831. W. C. (1985): An Examination of Procedures for Determining the Number of Clusters in a Data Set.

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