© 2015

Machine Learning and Data Mining in Pattern Recognition

11th International Conference, MLDM 2015, Hamburg, Germany, July 20-21, 2015, Proceedings

  • Petra Perner


  • Up-to-date results

  • Fast track conference proceedings

  • State-of-the-art report

Conference proceedings MLDM 2015

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9166)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 9166)

Table of contents

  1. Front Matter
    Pages I-IX
  2. Graph Mining

    1. Front Matter
      Pages 1-1
    2. Kaspar Riesen, Miquel Ferrer, Rolf Dornberger, Horst Bunke
      Pages 3-16
    3. Nimit Dhulekar, Srinivas Nambirajan, Basak Oztan, Bülent Yener
      Pages 32-52
  3. Classification and Regression

    1. Front Matter
      Pages 53-53
    2. Søren Atmakuri Davidsen, E. Sreedevi , M. Padmavathamma
      Pages 55-69
    3. Chao Tan, Jihong Guan, Shuigeng Zhou
      Pages 70-83
  4. Sentiment Analysis

    1. Front Matter
      Pages 85-85
    2. Gayane Shalunts, Gerhard Backfried
      Pages 87-97
    3. Angelo Corallo, Laura Fortunato, Marco Matera, Marco Alessi, Alessio Camillò, Valentina Chetta et al.
      Pages 98-112
  5. Data Preparation and Missing Values

    1. Front Matter
      Pages 113-113
    2. Tommi Kärkkäinen, Mirka Saarela
      Pages 140-154
  6. Association and Sequential Rule Mining

    1. Front Matter
      Pages 155-155
    2. Souleymane Zida, Philippe Fournier-Viger, Cheng-Wei Wu, Jerry Chun-Wei Lin, Vincent S. Tseng
      Pages 157-171
  7. Support Vector Machines

About these proceedings


This book constitutes the refereed proceedings of the 11th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2015, held in Hamburg, Germany, in July 2015.

The 41 full papers presented were carefully reviewed and selected from 123 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multimedia data types such as image mining, text mining, video mining and Web mining.


Association rule mining Bayesian network Biclustering Big data Bioinformatics Data cloud geometry Data mining Ensemble method Genetic algorithms Image mining Knowledge discovery in databases Machine learning Ontology Recommendation Robust statistics Semi-supervised learning Social network analysis Support vector machines Uncertainty Visualization

Editors and affiliations

  • Petra Perner
    • 1
  1. 1.IBaILeipzigGermany

Bibliographic information

Industry Sectors
IT & Software
Consumer Packaged Goods
Finance, Business & Banking
Energy, Utilities & Environment
Oil, Gas & Geosciences