Advances in data analysis: proceedings of the 30th Annual - download pdf or read online

By Reinhold Decker, Hans-Joachim Lenz

ISBN-10: 3540709800

ISBN-13: 9783540709800

The e-book specializes in exploratory info research, studying of latent buildings in datasets, and unscrambling of information. It covers a large diversity of equipment from multivariate statistics, clustering and class, visualization and scaling in addition to from information and time sequence research. It offers new techniques for info retrieval and knowledge mining. moreover, the booklet reviews not easy purposes in advertising and administration technology, banking and finance, bio- and wellbeing and fitness sciences, linguistics and textual content research, statistical musicology and sound type, in addition to archaeology. designated emphasis is wear interdisciplinary learn and the interplay among thought and perform.

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Additional resources 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): Choosing the Number of Component Clusters in the Mixture-Model Using a New Informational Complexity Criterion of the InverseFisher Information Matrix. In: O. Opitz, B. Lausen and R. ): Information and Classification, Concepts, Methods and Applications. Springer, Berlin, 40–54. , SMYTH, P. and WHITE, S. (2003): Visualization of Navigation Patterns on a Web Site Using Model-Based Clustering. Data Mining and Knowledge Discovery, 7, 399–424. R. G. (2004): Modeling Dynamic Effects in Repeated-measures Experiments Involving Preference/Choice: An Illustration Involving Stated Preference Analysis.

The third part describes the classification process for symbolic data. In the next part cluster quality indexes are compared on 100 sets of symbolic data with known structures and for three clustering methods. Furthermore, there is a short summary which of them most accu- 32 Andrzej Dudek rately represents the structure of the clusters. Finally some conclusions and remarks are given. 2 Clustering methods for symbolic data Symbolic data, unlike classical data, are more complex than tables of numeric values.

K) }, taking into account that Y is missing. For this purpose, we adopt the marginal maximum a posteriori criterion, obtained by marginalizing out the hidden labels; thus, since by Bayes law p(X , Y, z|φ, α) = p(X |Y, φ) P (Y|z) p(z|α), z, φ, α = arg max z,φ,α p(X |Y, φ) P (Y|z) p(z|α), Y where the sum is over all the possible label configurations, and we are assuming flat priors for φ and α. , McLachlan and Krishnan (1997)), that is, by iterating the following two steps (until some convergence criterion is met): E-step: Compute the conditional expectation of the complete log-posterior, given the current estimates (z, φ, α) and the observations X : Q(z, φ, α|z, φ, α) = EY [log p(X , Y, z|φ, α)|z, φ, α, X ].

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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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