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Advances in Intelligent Data Analysis X

10th International Symposium, IDA 2011, Porto, Portugal, October 29-31, 2011, Proceedings

  • Conference proceedings
  • © 2011

Overview

Editors:
  1. João Gama

    1. Faculty of Economics, LIAAD-INESC Porto, L.A., University of Porto, Porto, Portugal

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  2. Elizabeth Bradley

    1. Department of Computer Science, University of Colorado, Boulder, USA

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  3. Jaakko Hollmén

    1. Department of Information and Computer Science, Aalto University School of Science, Aalto, Finland

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 7014)

Part of the book sub series: Information Systems and Applications, incl. Internet/Web, and HCI (LNISA)

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Conference proceedings info: IDA 2011.

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About this book

This book constitutes the refereed proceedings of the 10th International Conference on Intelligent Data Analysis, IDA 2011, held in Porto, Portugal, in October 2011. The 19 revised full papers and 16 revised poster papers resented together with 3 invited papers were carefully reviewed and selected from 73 submissions. All current aspects of intelligent data analysis are addressed, particularly intelligent support for modeling and analyzing complex, dynamical systems. The papers offer intelligent support for understanding evolving scientific and social systems including data collection and acquisition, such as crowd sourcing; data cleaning, semantics and markup; searching for data and assembling datasets from multiple sources; data processing, including workflows, mixed-initiative data analysis, and planning; data and information fusion; incremental, mixed-initiative model development, testing and revision; and visualization and dissemination of results; etc.

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Table of contents (38 papers)

  1. Front Matter

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  2. Invited Papers

    1. Bisociative Knowledge Discovery

      • Michael R. Berthold
      Pages 1-7

    2. Computational Sustainability

      • Carla P. Gomes
      Pages 8-8

    3. Intelligent Data Analysis: Keeping Pace with Technological Advances

      • Xiaohui Liu
      Pages 9-9

  3. Selected Contributions

    1. Comparative Analysis of Power Consumption in University Buildings Using envSOM

      • Serafín Alonso, Manuel Domínguez, Miguel Angel Prada, Mika Sulkava, Jaakko Hollmén
      Pages 10-21

    2. Context-Aware Collaborative Data Stream Mining in Ubiquitous Devices

      • João Bártolo Gomes, Mohamed Medhat Gaber, Pedro A. C. Sousa, Ernestina Menasalvas
      Pages 22-33

    3. Intra-Firm Information Flow: A Content-Structure Perspective

      • Yakir Berchenko, Or Daliot, Nir N. Brueller
      Pages 34-42

    4. Mining Fault-Tolerant Item Sets Using Subset Size Occurrence Distributions

      • Christian Borgelt, Tobias Kötter
      Pages 43-54

    5. Finding Ensembles of Neurons in Spike Trains by Non-linear Mapping and Statistical Testing

      • Christian Braune, Christian Borgelt, Sonja Grün
      Pages 55-66

    6. Towards Automatic Pathway Generation from Biological Full-Text Publications

      • Ekaterina Buyko, Jörg Linde, Steffen Priebe, Udo Hahn
      Pages 67-79

    7. Online Writing Data Representation: A Graph Theory Approach

      • Gilles Caporossi, Christophe Leblay
      Pages 80-89

    8. Online Evaluation of Email Streaming Classifiers Using GNUsmail

      • José M. Carmona-Cejudo, Manuel Baena-García, José del Campo-Ávila, Albert Bifet, João Gama, Rafael Morales-Bueno
      Pages 90-100

    9. The Dynamic Stage Bayesian Network: Identifying and Modelling Key Stages in a Temporal Process

      • Stefano Ceccon, David Garway-Heath, David Crabb, Allan Tucker
      Pages 101-112

    10. Mining Train Delays

      • Boris Cule, Bart Goethals, Sven Tassenoy, Sabine Verboven
      Pages 113-124

    11. Robustness of Change Detection Algorithms

      • Tamraparni Dasu, Shankar Krishnan, Gina Maria Pomann
      Pages 125-137

    12. GaMuSo: Graph Base Music Recommendation in a Social Bookmarking Service

      • Jeroen De Knijf, Anthony Liekens, Bart Goethals
      Pages 138-149

    13. Resampling-Based Change Point Estimation

      • Jelena Fiosina, Maksims Fiosins
      Pages 150-161

    14. Learning about the Learning Process

      • João Gama, Petr Kosina
      Pages 162-172

    15. Predicting Computer Performance Dynamics

      • Joshua Garland, Elizabeth Bradley
      Pages 173-184

    16. Prototype-Based Classification of Dissimilarity Data

      • Barbara Hammer, Bassam Mokbel, Frank-Michael Schleif, Xibin Zhu
      Pages 185-197
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Other volumes

  1. Advances in Intelligent Data Analysis X

Editors and Affiliations

  • Faculty of Economics, LIAAD-INESC Porto, L.A., University of Porto, Porto, Portugal

    João Gama

  • Department of Computer Science, University of Colorado, Boulder, USA

    Elizabeth Bradley

  • Department of Information and Computer Science, Aalto University School of Science, Aalto, Finland

    Jaakko Hollmén

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