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

Proceedings of the 34th Annual Conference of the Gesellschaft für Klassifikation e. V., Karlsruhe, July 21 - 23, 2010

  • Conference proceedings
  • © 2012

Overview

Editors:
  1. Wolfgang A. Gaul

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

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  2. Andreas Geyer-Schulz

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

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  3. Lars Schmidt-Thieme

    1. , Information Systems, University ofHildesheim, Hildesheim, Germany

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  4. Jonas Kunze

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

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  • Focus on the interface between data analysis, computer science, and operations research
  • Cutting edge methods and results for many advanced data analysis problems
  • Covers traditional research directions as well as wide area of currently interesting application fields
  • Includes supplementary material: sn.pub/extras

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About this book

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.

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Table of contents (60 papers)

  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
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Editors and Affiliations

  • 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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