Mining Complex Data

  • Djamel A. Zighed
  • Shusaku Tsumoto
  • Zbigniew W. Ras
  • Hakim Hacid

Part of the Studies in Computational Intelligence book series (SCI, volume 165)

Table of contents

  1. Front Matter
  2. General Aspects of Complex Data

    1. Front Matter
      Pages 1-1
    2. Brigitte Mathiak, Andreas Kupfer, Silke Eckstein
      Pages 3-22
    3. Emna Bahri, Stephane Lallich, Nicolas Nicoloyannis, Maddouri Mondher
      Pages 41-54
    4. Lamis Hawarah, Ana Simonet, Michel Simonet
      Pages 55-74
    5. Thanh-Nghi Do, François Poulet
      Pages 75-91
  3. Rules Extraction

    1. Front Matter
      Pages 93-93
    2. Hidenao Abe, Shusaku Tsumoto, Miho Ohsaki, Takahira Yamaguchi
      Pages 95-111
    3. Marcela X. Ribeiro, Andre G. R. Balan, Joaquim C. Felipe, Agma J. M. Traina, Caetano Traina Jr.
      Pages 113-131
    4. Zbigniew W. Raś, Li-Shiang Tsay, Agnieszka Dardzińska
      Pages 153-163
  4. Graph Data Mining

    1. Front Matter
      Pages 165-165
    2. Stanislav Bartoň, Pavel Zezula
      Pages 167-188
    3. Christian Borgelt, Thorsten Meinl
      Pages 189-205
    4. Nan Du, Bin Wu, Liutong Xu, Bai Wang, Pei Xin
      Pages 207-221
    5. Kazumi Saito, Takeshi Yamada, Kazuhiro Kazama
      Pages 243-257
  5. Data Clustering

    1. Front Matter
      Pages 259-259
    2. Toshihiro Kamishima, Shotaro Akaho
      Pages 261-279
    3. Ahmad El Sayed, Hakim Hacid, Djamel Zighed
      Pages 281-300
  6. Back Matter

About this book


The aim of this book is to gather the most recent works that address issues related to the concept of mining complex data. The whole knowledge discovery process being involved, our goal is to provide researchers dealing with each step of this process by key entries. Actually, managing complex data within the KDD process implies to work on every step, starting from the pre-processing (e.g. structuring and organizing) to the visualization and interpretation (e.g. sorting or filtering) of the results, via the data mining methods themselves (e.g. classification, clustering, frequent patterns extraction, etc.). The papers presented here are selected from the workshop papers held yearly since 2006.

The book is composed of four parts and a total of sixteen chapters. Part I gives a general view of complex data mining by illustrating some situations and the related complexity. It contains five chapters. Chapter 1 illustrates the problem of analyzing the scientific literature. The chapter gives some background to the various techniques in this area, explains the necessary pre-processing steps involved, and presents two case studies, one from image mining and one from table identification.


Extension algorithms classification clustering computational intelligence data mining decision tree fuzzy system genetic algorithms kernel knowledge knowledge discovery layout learning visualization

Editors and affiliations

  • Djamel A. Zighed
    • 1
  • Shusaku Tsumoto
    • 2
  • Zbigniew W. Ras
    • 3
  • Hakim Hacid
    • 1
  1. 1.University of LyonLyonFrance
  2. 2.Shimane UniversityShimaneJapan
  3. 3.University of North CarolinaCharlotteUSA

Bibliographic information

  • DOI
  • Copyright Information Springer Berlin Heidelberg 2009
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-540-88066-0
  • Online ISBN 978-3-540-88067-7
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site
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