Challenges at the Interface of Data Analysis, Computer Science, and Optimization

1. Front Matter

   Pages i-xiv

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2. Classification, Cluster Analysis, and Multidimensional Scaling

   1. Front Matter

      Pages 1-1

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   2. Fuzzification of Agglomerative Hierarchical Crisp Clustering Algorithms

      • Mathias Bank, Friedhelm Schwenker

      Pages 3-11

   3. An EM Algorithm for the Student-t Cluster-Weighted Modeling

      • Salvatore Ingrassia, Simona C. Minotti, Giuseppe Incarbone

      Pages 13-21

   4. Analysis of Distribution Valued Dissimilarity Data

      • Masahiro Mizuta, Hiroyuki Minami

      Pages 23-28

   5. An Overall Index for Comparing Hierarchical Clusterings

      • I. Morlini, S. Zani

      Pages 29-36

   6. An Accelerated K-Means Algorithm Based on Adaptive Distances

      • Hans-Joachim Mucha, Hans-Georg Bartel

      Pages 37-47

   7. Bias-Variance Analysis of Local Classification Methods

      • Julia Schiffner, Bernd Bischl, Claus Weihs

      Pages 49-57

   8. Effect of Data Standardization on the Result of k-Means Clustering

      • Kensuke Tanioka, Hiroshi Yadohisa

      Pages 59-67

   9. A Case Study on the Use of Statistical Classification Methods in Particle Physics

      • Claus Weihs, Olaf Mersmann, Bernd Bischl, Arno Fritsch, Heike Trautmann, Till Moritz Karbach et al.

      Pages 69-77

   10. Problems of Fuzzy c-Means Clustering and Similar Algorithms with High Dimensional Data Sets

       • Roland Winkler, Frank Klawonn, Rudolf Kruse

       Pages 79-87

3. Quantification Theory

   1. Front Matter

      Pages 89-89

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   2. Reduced Versus Complete Space Configurations in Total Information Analysis

      • José G. Clavel, Shizuhiko Nishisato

      Pages 91-99

   3. A Geometrical Interpretation of the Horseshoe Effect in Multiple Correspondence Analysis of Binary Data

      • Takashi Murakami

      Pages 101-108

   4. Quantification Theory: Reminiscence and a Step Forward

      • Shizuhiko Nishisato

      Pages 109-119

4. Analysis of m-Mode n-Way and Asymmetric Data

   1. Front Matter

      Pages 121-121

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   2. Modelling Rater Differences in the Analysis of Three-Way Three-Mode Binary Data

      • Michel Meulders

      Pages 123-131

   3. Reconstructing One-Mode Three-way Asymmetric Data for Multidimensional Scaling

      • Atsuho Nakayama, Akinori Okada

      Pages 133-141

   4. Analysis of Car Switching Data by Asymmetric Multidimensional Scaling Based on Singular Value Decomposition

      • Akinori Okada

      Pages 143-150

   5. Visualization of Asymmetric Clustering Result with Digraph and Dendrogram

      • Yuichi Saito, Hiroshi Yadohisa

      Pages 151-159

This volume provides approaches and solutions to challenges occurring at the interface of research fields such as data analysis, computer science, operations research, and statistics. It includes theoretically oriented contributions as well as papers from various application areas, where knowledge from different research directions is needed to find the best possible interpretation of data for the underlying problem situations. Beside traditional classification research, the book focuses on current interests in fields such as the analysis of social relationships as well as statistical musicology.

• Applied Statistics
• Classification
• Clustering
• Data Analysis
• data structures

• Fak. Wirtschaftswissenschaften, Inst. Entscheidungstheorieund, Universität Karlsruhe (TH), Karlsruhe, Germany

  Wolfgang A. Gaul

• Insitute for Information Systems, and Management (IISM), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany

  Andreas Geyer-Schulz

• , Information Systems, University ofHildesheim, Hildesheim, Germany

  Lars Schmidt-Thieme

• Institute for Information Systems, and Management (IISM), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany

  Jonas Kunze

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