From Data and Information Analysis to Knowledge Engineering: by Peter Bühlmann (auth.), Professor Dr. Myra Spiliopoulou, PDF

By Peter Bühlmann (auth.), Professor Dr. Myra Spiliopoulou, Professor Dr. Rudolf Kruse, Dr. Christian Borgelt, Jun.-Professor Dr. Andreas Nürnberger, Professor Dr. Wolfgang Gaul (eds.)

ISBN-10: 3540313133

ISBN-13: 9783540313137

ISBN-10: 3540313141

ISBN-13: 9783540313144

This quantity collects revised types of chosen papers awarded through the twenty ninth Annual convention of the Gesellschaft f?r Klassifikation (GfKl), the German category Society, held on the Otto-von-Guericke-University of Magdeburg, Germany, in March 2005.

In addition to papers on conventional topics resembling category, Clustering, and knowledge research, there are numerous papers on quite a lot of subject matters with a robust relation to laptop technology: textual content Mining, net Mining, Fuzzy info research, IT safeguard, Adaptivity and Personalization, and Visualization. Application-oriented issues contain Economics, advertising, Banking and Finance, drugs, Bioinformatics, Biostatistics, and song Analysis.

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This quantity collects revised types of chosen papers provided in the course of the twenty ninth Annual convention of the Gesellschaft f? r Klassifikation (GfKl), the German category Society, held on the Otto-von-Guericke-University of Magdeburg, Germany, in March 2005. as well as papers on conventional matters akin to type, Clustering, and knowledge research, there are various papers on a variety of issues with a powerful relation to machine technology: textual content Mining, net Mining, Fuzzy facts research, IT defense, Adaptivity and Personalization, and Visualization.

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Extra resources for From Data and Information Analysis to Knowledge Engineering: Proceedings of the 29th Annual Conference of the Gesellschaft für Klassifikation e.V. University of Magdeburg, March 9–11, 2005

Example text

75. Making it larger would occasionally cause 4 to be found when Cluster 5 was split exactly into 2 chunks. For nine classes, five classes were always found for a percentage of 80 up to about 90. There might be two or three empty clusters. The other non-clusters were very lightly populated with less than 15 examples closest to their centroid in the usual case. As the percentage got too high it would cause a class split into two, to occasionally be missed resulting in four clusters. 11, class 4 is split into two clusters with 107 and 86 examples in each, respectively.

2004): Least angle regression. Ann. , 32, 407–499 (with discussion). FREUND, Y. E. (1996): Experiments with a new boosting algorithm. In: Machine Learning: Proc. Thirteenth International Conference. Morgan Kauffman, San Francisco, 148–156. H. (2001): Greedy function approximation: a gradient boosting machine. Ann. , 29, 1189–1232. , HASTIE, T. and TIBSHIRANI, R. (2000): Additive logistic regression: a statistical view of boosting. Ann. , 28, 337–407 (with discussion). GREENSHTEIN, E. and RITOV, Y.

1991): Validity measure for fuzzy clustering. IEEE Trans. on Pattern Analysis and Machine Intelligence, 3(8):841–846. SolEuNet: Selected Data Mining Techniques and Applications Nada Lavraˇc1,2 1 2 Joˇzef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia Nova Gorica Polytechnic, Vipavska 13, 5000 Nova Gorica, Slovenia Abstract. Data mining is concerned with the discovery of interesting patterns and models in data. In practice, data mining has become an established technology with applications in a wide range of areas that include marketing, health care, finance, environmental planning, up to applications in e-commerce and e-science.

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From Data and Information Analysis to Knowledge Engineering: Proceedings of the 29th Annual Conference of the Gesellschaft für Klassifikation e.V. University of Magdeburg, March 9–11, 2005 by Peter Bühlmann (auth.), Professor Dr. Myra Spiliopoulou, Professor Dr. Rudolf Kruse, Dr. Christian Borgelt, Jun.-Professor Dr. Andreas Nürnberger, Professor Dr. Wolfgang Gaul (eds.)


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