Get Advances in data analysis: proceedings of the 30th Annual PDF

By Reinhold Decker, Hans-Joachim Lenz

ISBN-10: 3540709800

ISBN-13: 9783540709800

The ebook makes a speciality of exploratory information research, studying of latent buildings in datasets, and unscrambling of information. It covers a extensive diversity of tools from multivariate information, clustering and category, visualization and scaling in addition to from facts and time sequence research. It presents new methods for info retrieval and knowledge mining. in addition, the publication studies tough functions in advertising and administration technological know-how, banking and finance, bio- and future health sciences, linguistics and textual content research, statistical musicology and sound type, in addition to archaeology. distinctive emphasis is wear interdisciplinary study and the interplay among thought and perform.

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Additional 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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This approach has the advantage to be computationally inexpensive while being an a priori method, independent from the clustering technique. 1 Introduction Clustering is a technique used in the analysis of microarray gene expression data as a preprocessing step, in functional genomic for example, or as the main discriminating tool in the tumor classification study (Dudoit et al. (2002)). While in recent years many clustering methods were developed, it is acknowledged that the reliability of allocation of units to a cluster and the computation of the number of clusters are questions waiting for a joint theoretical and practical validation (Dudoit et al.

The experiment showed that the most adequate ones for this kind of data are the Hubert and Levine and the Baker and Hubert indexes. We can assume that the usage of these indexes in case of real symbolic data validation should also give good results. The preliminary experiments with real symbolic data sets, done by the author, also confirm the quality of these indexes in the symbolic data case. Cluster Quality Indexes for Symbolic Classification – An Examination 37 The results can be explained by the fact that Hubert and Levine and the Baker and Hubert indexes are based on distance matrices and for them, limitations of symbolic methods, described in section 2, do not exist.

J. and PEEL, D. (2000): Finite Mixture Models. John Wiley & Sons, New York. S. (1990): Mixed Markov and Latent Markov Modelling Applied to Brand Choice Behavior. International Journal of Research in Marketing, 7, 5– 19. J. T. (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.

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Advances in data analysis: proceedings of the 30th Annual Conference of The Gesellschaft fur Klassifikation e.V., Freie Universitat Berlin, March 8-10, 2006 by Reinhold Decker, Hans-Joachim Lenz


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