Formal Concept Analysis

6th International Conference, ICFCA 2008, Montreal, Canada, February 25-28, 2008. Proceedings

  • Editors
  • Raoul Medina
  • Sergei Obiedkov

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4933)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 4933)

Table of contents

  1. Front Matter
  2. Jean-François Boulicaut, Jérémy Besson
    Pages 14-31
  3. Sebastian Rudolph
    Pages 32-45
  4. Johanna Völker, Sebastian Rudolph
    Pages 62-77
  5. Jaume Baixeries
    Pages 90-105
  6. Christian Zschalig
    Pages 106-123
  7. Francisco J. Valverde-Albacete, Carmen Peláez-Moreno
    Pages 124-139
  8. Pierre Colomb, Lhouari Nourine
    Pages 140-149
  9. Miki Hermann, Barış Sertkaya
    Pages 158-168
  10. Petko Valtchev, Vincent Duquenne
    Pages 182-198
  11. Bernhard Ganter
    Pages 199-216
  12. Bernhard Ganter, Sergei O. Kuznetsov
    Pages 217-228
  13. Susanne Motameny, Beatrix Versmold, Rita Schmutzler
    Pages 229-240
  14. Olivier Bedel, Sébastien Ferré, Olivier Ridoux
    Pages 241-257
  15. Nicolas Jay, François Kohler, Amedeo Napoli
    Pages 258-272

About these proceedings

Introduction

Formal Concept Analysis (FCA) is a mathematical theory of concepts and c- ceptualhierarchyleadingtomethodsforconceptuallyanalyzingdataandkno- edge. The theoryitselfstronglyreliesonorderandlatticetheory,whichhasbeen studied by mathematicians over decades. FCA proved itself highly relevant in several applications from the beginning, and, over the last years, the range of applicationshaskeptgrowing. The mainreasonfor this comesfromthe fact that our modern society has turned into an “information” society. After years and years of using computers, companies realized they had stored gigantic amounts of data. Then, they realized that this data, just rough information for them, might become a real treasure if turned into knowledge. FCA is particularly well suited for this purpose. From relational data, FCA can extract implications, - pendencies, concepts and hierarchies of concepts, and thus capture part of the knowledge hidden in the data. The ICFCA conference series gathers researchers from all over the world, being the main forum to present new results in FCA and related ?elds. These results range from theoretical novelties to advances in FCA-related algorithmic issues, as well as application domains of FCA. ICFCA 2008 was in the same vein as its predecessors: high-quality papers and presentations, the place of real debate and exchange of ideas. ICFCA 2008 contributed to strengthening the links between theory and applications. The high quality of the presentations was the result of the remarkable work of the authors and the reviewers. We wish to thank the reviewers for all their valuable comments, which helped the authors to improve their presentations.

Keywords

Analysis DOM algorithms knowledge management learning machine learning visualization

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-78137-0
  • Copyright Information Springer-Verlag Berlin Heidelberg 2008
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-78136-3
  • Online ISBN 978-3-540-78137-0
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book