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Principles of Data Mining and Knowledge Discovery

4th European Conference, PKDD, 2000, Lyon, France, September 13-16, 2000 Proceedings

  • Conference proceedings
  • © 2000

Overview

Editors:
  1. Djamel A. Zighed

    1. Laboratoire ERIC, Université Lyon 2, Bron, France

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  2. Jan Komorowski

    1. Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim, Norway

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    You can also search for this editor in PubMed   Google Scholar

  3. Jan Żytkow

    1. Department of Computer Science, University of North Carolina, Charlotte, USA

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 1910)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

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Table of contents (86 papers)

  1. Instance-Based Discovery

    1. Context-Based Similarity Measures for Categorical Databases

      • Gautam Das, Heikki Mannila
      Pages 201-210

    2. A Mixed Similarity Measure in Near-Linear Computational Complexity for Distance-Based Methods

      • Ngoc Binh Nguyen, Tu Bao Ho
      Pages 211-220

    3. Fast Feature Selection using Partial Correlation for Multi-valued Attributes

      • S. Lallich, R. Rakotomalala
      Pages 221-231

  2. Clustering and Classification

    1. Fast Hierarchical Clustering Based on Compressed Data and OPTICS

      • Markus M. Breunig, Hans-Peter Kriegel, Jörg Sander
      Pages 232-242

    2. Accurate Recasting of Parameter Estimation Algorithms Using Sufficient Statistics for Efficient Parallel Speed-Up

      • Bin Zhang, Meichun Hsu, George Forman
      Pages 243-254

    3. Predictive Performance of Weighted Relative Accuracy

      • Ljupčo Todorovski, Peter Flach, Nada Lavrač
      Pages 255-264

    4. Quality Scheme Assessment in the Clustering Process

      • M. Halkidi, M. Vazirgiannis, Y. Batistakis
      Pages 265-276

  3. Time Series

    1. Algorithm for Matching Sets of Time Series

      • Iztok Savnik, Georg Lausen, Hans-Peter Kahle, Heinrich Spiecker, Sebastian Hein
      Pages 277-288

    2. MSTS: A System for Mining Sets of Time Series

      • Georg Lausen, Iztok Savnik, Aldar Dougarjapov
      Pages 289-298

    3. Learning First Order Logic Time Series Classifiers: Rules and Boosting

      • Juan J. Rodríguez, Carlos J. Alonso, Henrik Boström
      Pages 299-308

  4. Posters

    1. Learning Right Sized Belief Networks by Means of a Hybrid Methodology

      • Silvia Acid, Luis M. De Campos
      Pages 309-315

    2. Algorithms for Mining Share Frequent Itemsets Containing Infrequent Subsets

      • Brock Barber, Howard J. Hamilton
      Pages 316-324

    3. Discovering Task Neighbourhoods through Landmark Learning Performances

      • Hilan Bensusan, Christophe Giraud-Carrier
      Pages 325-330

    4. Induction of Multivariate Decision Trees by Using Dipolar Criteria

      • Leon Bobrowski, Marek Kretowski
      Pages 331-336

    5. Inductive Logic Programming in Clementine

      • Sam Brewer, Tom Khabaza
      Pages 337-344

    6. A Genetic Algorithm-Based Solution for the Problem of Small Disjuncts

      • Deborah R. Carvalho, Alex A. Freitas
      Pages 345-352

    7. Clustering Large, Multi-level Data Sets: An Approach Based on Kohonen Self Organizing Maps

      • Antonio Ciampi, Yves Lechevallier
      Pages 353-358

    8. Trees and Induction Graphs for Multivariate Response

      • Antonio Ciampi, Djamel A. Zighed, Jérémy Clech
      Pages 359-366

    9. CEM - A Program for Visualization and Discovery in Email

      • Richard Cole, Peter Eklund, Gerd Stumme
      Pages 367-374

    10. Image Access and Data Mining: An Approach

      • Chabane Djeraba
      Pages 375-380
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Keywords

Editors and Affiliations

  • Laboratoire ERIC, Université Lyon 2, Bron, France

    Djamel A. Zighed

  • Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim, Norway

    Jan Komorowski

  • Department of Computer Science, University of North Carolina, Charlotte, USA

    Jan Żytkow

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