Skip to main content
SpringerLink
Log in
SpringerLink
Log in

Selected Contributions in Data Analysis and Classification

  • Book
  • © 2007

Overview

Editors:
  1. Paula Brito

    1. Faculty of Economics, University of Porto, Porto, Portugal

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  2. Guy Cucumel

    1. ESG UQAM, Montreal, Canada

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  3. Patrice Bertrand

    1. Department Lussi, ENST Bretagne, Cesson-Sévigné Cedex, France

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  4. Francisco Carvalho

    1. Centre of Computer Science (CIn), Federal University of Pernambuco (UFPE), Recife-PE, Brazil

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (59 chapters)

  1. Front Matter

    Pages I-XIII
    Download chapter PDF

  2. Analysis of Symbolic Data

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Dependencies and Variation Components of Symbolic Interval-Valued Data

      • Lynne Billard
      Pages 3-12

    3. On the Analysis of Symbolic Data

      • Paula Brito
      Pages 13-22

    4. Symbolic Analysis to Learn Evolving CyberTraffic

      • Costantina Caruso, Donato Malerba
      Pages 23-33

    5. A Clustering Algorithm for Symbolic Interval Data Based on a Single Adaptive Hausdorff Distance

      • Francisco de A. T. de Carvalho
      Pages 35-44

    6. An Agglomerative Hierarchical Clustering Algorithm for Improving Symbolic Object Retrieval

      • Floriana Esposito, Claudia d’Amato
      Pages 45-53

    7. 3WaySym-Scal: Three-Way Symbolic Multidimensional Scaling

      • Patrick J. F. Groenen, Suzanne Winsberg
      Pages 55-67

    8. Clustering and Validation of Interval Data

      • André Hardy, Joffray Baune
      Pages 69-81

    9. Building Symbolic Objects from Data Streams

      • Georges Hébrail, Yves Lechevallier
      Pages 83-94

    10. Feature Clustering Method to Detect Monotonic Chain Structures in Symbolic Data

      • Manabu Ichino
      Pages 95-102

    11. Symbolic Markov Chains

      • Monique Noirhomme-Fraiture, Etienne Cuvelier
      Pages 103-111

    12. Quality Issues in Symbolic Data Analysis

      • Haralambos Papageorgiou, Maria Vardaki
      Pages 113-122

    13. Dynamic Clustering of Histogram Data: Using the Right Metric

      • Rosanna Verde, Antonio Irpino
      Pages 123-134

  3. Clustering Methods

    1. Front Matter

      Pages 135-135
      Download chapter PDF

    2. Beyond the Pyramids: Rigid Clustering Systems

      • Jean-Pierre Barthélemy, Gentian Gusho, Christophe Osswald
      Pages 137-150

    3. Indirect Blockmodeling of 3-Way Networks

      • Vladimir Batagelj, Anuška Ferligoj, Patrick Doreian
      Pages 151-159

    4. Clustering Methods: A History of k-Means Algorithms

      • Hans-Hermann Bock
      Pages 161-172

    5. Overlapping Clustering in a Graph Using k-Means and Application to Protein Interactions Networks

      • Irène Charon, Lucile Denoeud, Olivier Hudry
      Pages 173-182

    6. Species Clustering via Classical and Interval Data Representation

      • Marie Chavent
      Pages 183-191
Back to top

Keywords

About this book

By inviting me to write a preface, the organizers of the event in honour of Edwin Diday, have expressed their a?ection and I appreciate this very much. This gives me an opportunity to express my friendship and admiration for Edwin Diday, and I wrote this foreword with pleasure. My ?rst few meetings withEdwinDidaydatebackto1965through1975,daysofthedevelopmentof French statistics. This was a period when access to computers revolutionized the practice of statistics. This does not refer to individual computers or to terminals that have access to powerful networks. This was the era of the ?rst university calculation centres that one accessed over a counter. One would deposit cards on which program and data were punched in and come back a few hours or days later for the results. Like all those who used linear data analysis, the computer enabled me to calculate for each data set the value of mathematical objects (eigenvalues and eigenvectors for example) whose optimality properties had been demonstrated by mathematicians. It was - ready a big step to be able to do this in concrete experimental situations. With Dynamic Clustering Algorithm, Edwin Diday allowed us to discover that computers could be more than just a way of giving numerical values to known mathematical objects. Besides the e?ciency of the solutions he built, he led us to integrate the access to computers di?erently in the research and practice of data analysis.

Editors and Affiliations

  • Faculty of Economics, University of Porto, Porto, Portugal

    Paula Brito

  • ESG UQAM, Montreal, Canada

    Guy Cucumel

  • Department Lussi, ENST Bretagne, Cesson-Sévigné Cedex, France

    Patrice Bertrand

  • Centre of Computer Science (CIn), Federal University of Pernambuco (UFPE), Recife-PE, Brazil

    Francisco Carvalho

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation