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Titlebook: German-Japanese Interchange of Data Analysis Results; Wolfgang Gaul,Andreas Geyer-Schulz,Akinori Okada Conference proceedings 2014 Springe

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書目名稱German-Japanese Interchange of Data Analysis Results
編輯Wolfgang Gaul,Andreas Geyer-Schulz,Akinori Okada
視頻videohttp://file.papertrans.cn/385/384375/384375.mp4
概述Focuses on the interface between Data Analysis and Computer Science.Presents innovative methods and results for many data analysis problems.Offers applications from a wide area of disciplines?.Include
叢書名稱Studies in Classification, Data Analysis, and Knowledge Organization
圖書封面Titlebook: German-Japanese Interchange of Data Analysis Results;  Wolfgang Gaul,Andreas Geyer-Schulz,Akinori Okada Conference proceedings 2014 Springe
描述This volume focuses on innovative approaches and recent developments in clustering, analysis of data and models, and applications: The first part of the book covers a broad range of innovations in the area of clustering, from algorithmic innovations for graph clustering to new visualization and evaluation techniques. The second part addresses new developments in data and decision analysis (conjoint analysis, non-additive utility functions, analysis of asymmetric relationships, and regularization techniques). The third part is devoted to the application of innovative data analysis methods in the life-sciences, the social sciences and in engineering. All contributions in this volume are revised and extended versions of selected papers presented in the German/Japanese Workshops at Karlsruhe (2010) and Kyoto (2012).?
出版日期Conference proceedings 2014
關(guān)鍵詞Applied Statistics; Clustering; Computing Methodologies; Data Analysis
版次1
doihttps://doi.org/10.1007/978-3-319-01264-3
isbn_softcover978-3-319-01263-6
isbn_ebook978-3-319-01264-3Series ISSN 1431-8814 Series E-ISSN 2198-3321
issn_series 1431-8814
copyrightSpringer International Publishing Switzerland 2014
The information of publication is updating

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Examining Intelligence-Led Policingetermine a suitable (hopefully optimum) clustering by statistical tools like maximum likelihood and optimization algorithms. In particular, we will consider models with class-specific Gaussian processes and Markov chains.
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Model-Based Clustering Methods for Time Seriesetermine a suitable (hopefully optimum) clustering by statistical tools like maximum likelihood and optimization algorithms. In particular, we will consider models with class-specific Gaussian processes and Markov chains.
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Analysis of Asymmetric Relationships Among Soft Drink Brandsnds along each dimension, and the inward tendency, which represents the strength of switching to a corresponding brand from the other brands along each dimension. The solution disclosed that the differences between diet and non-diet brands as well as between cola and lemon-lime brands played important roles in the brand switching.
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