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Data Analysis, Machine Learning and Knowledge Discovery

  • Conference proceedings
  • © 2014

Overview

Editors:
  1. Myra Spiliopoulou

    1. Faculty of Computer Science, Otto-von-Guericke-Universität Magdeburg, Magdeburg, Germany

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  2. Lars Schmidt-Thieme

    1. Institute of Computer Science, University of Hildesheim, Hildesheim, Germany

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  3. Ruth Janning

    1. Institute of Computer Science, University of Hildesheim, Hildesheim, Germany

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  • Focus on the commonalities concerning data analysis in computer science and in statistics
  • Emphasis on both methods (statistical analysis and machine learning) and applications (marketing, finance, bioinformatics, musicology, psychology)
  • Presentation of general methods and techniques that can be applied to a variety of fields?
  • Includes supplementary material: sn.pub/extras

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Table of contents (49 papers)

  1. Front Matter

    Pages i-xxi
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  2. AREA Statistics and Data Analysis: Classification, Cluster Analysis, Factor Analysis and Model Selection

    1. Front Matter

      Pages 1-1
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    2. On Limiting Donor Usage for Imputation of Missing Data via Hot Deck Methods

      • Udo Bankhofer, Dieter William Joenssen
      Pages 3-11

    3. The Most Dangerous Districts of Dortmund

      • Tim Beige, Thomas Terhorst, Claus Weihs, Holger Wormer
      Pages 13-21

    4. Benchmarking Classification Algorithms on High-Performance Computing Clusters

      • Bernd Bischl, Julia Schiffner, Claus Weihs
      Pages 23-31

    5. Visual Models for Categorical Data in Economic Research

      • Justyna Brzezińska
      Pages 33-40

    6. How Many Bee Species? A Case Study in Determining the Number of Clusters

      • Christian Hennig
      Pages 41-49

    7. Two-Step Linear Discriminant Analysis for Classification of EEG Data

      • Nguyen Hoang Huy, Stefan Frenzel, Christoph Bandt
      Pages 51-59

    8. Predictive Validity of Tracking Decisions: Application of a New Validation Criterion

      • Florian Klapproth, Sabine Krolak-Schwerdt, Thomas Hörstermann, Romain Martin
      Pages 61-69

    9. DDα-Classification of Asymmetric and Fat-Tailed Data

      • Tatjana Lange, Karl Mosler, Pavlo Mozharovskyi
      Pages 71-78

    10. The Alpha-Procedure: A Nonparametric Invariant Method for Automatic Classification of Multi-Dimensional Objects

      • Tatjana Lange, Pavlo Mozharovskyi
      Pages 79-86

    11. Support Vector Machines on Large Data Sets: Simple Parallel Approaches

      • Oliver Meyer, Bernd Bischl, Claus Weihs
      Pages 87-95

    12. Soft Bootstrapping in Cluster Analysis and Its Comparison with Other Resampling Methods

      • Hans-Joachim Mucha, Hans-Georg Bartel
      Pages 97-104

    13. Dual Scaling Classification and Its Application in Archaeometry

      • Hans-Joachim Mucha, Hans-Georg Bartel, Jens Dolata
      Pages 105-113

    14. Gamma-Hadron-Separation in the MAGIC Experiment

      • Tobias Voigt, Roland Fried, Michael Backes, Wolfgang Rhode
      Pages 115-124

  3. AREA Machine Learning and Knowledge Discovery: Clustering, Classifiers, Streams and Social Networks

    1. Front Matter

      Pages 125-125
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    2. Implementing Inductive Concept Learning For Cooperative Query Answering

      • Maheen Bakhtyar, Nam Dang, Katsumi Inoue, Lena Wiese
      Pages 127-134

    3. Clustering Large Datasets Using Data Stream Clustering Techniques

      • Matthew Bolaños, John Forrest, Michael Hahsler
      Pages 135-143

    4. Feedback Prediction for Blogs

      • Krisztian Buza
      Pages 145-152

    5. Spectral Clustering: Interpretation and Gaussian Parameter

      • Sandrine Mouysset, Joseph Noailles, Daniel Ruiz, Clovis Tauber
      Pages 153-162
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Keywords

About this book

Data analysis, machine learning and knowledge discovery are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and techniques that can be applied to a vast set of applications such as web and text mining, marketing, medicine, bioinformatics and business intelligence. This volume contains the revised versions of selected papers in the field of data analysis, machine learning and knowledge discovery presented during the 36th annual conference of the German Classification Society (GfKl). The conference was held at the University of Hildesheim (Germany) in August 2012. ​

Reviews

From the book reviews:

“The book is organized in seven parts … . The book is a very interesting collection of papers describing various approaches of data mining and machine learning on aspects from bioinformatics to music classification. It is an excellent addition to the field and it can be used as starting point for projects from undergraduate to post-graduate level.” (Irina Ioana Mohorianu, zbMATH, Vol. 1301, 2015)

Editors and Affiliations

  • Faculty of Computer Science, Otto-von-Guericke-Universität Magdeburg, Magdeburg, Germany

    Myra Spiliopoulou

  • Institute of Computer Science, University of Hildesheim, Hildesheim, Germany

    Lars Schmidt-Thieme, Ruth Janning

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