Machine Learning and Data Mining in Pattern Recognition

1. Front Matter

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2. Classification and Model Estimation

   1. On ECOC as Binary Ensemble Classifiers

      • J. Ko, E. Kim

      Pages 1-10

   2. Incremental Classification Rules Based on Association Rules Using Formal Concept Analysis

      • Anamika Gupta, Naveen Kumar, Vasudha Bhatnagar

      Pages 11-20

   3. Parameter Inference of Cost-Sensitive Boosting Algorithms

      • Yanmin Sun, A. K. C. Wong, Yang Wang

      Pages 21-30

   4. Finite Mixture Models with Negative Components

      • Baibo Zhang, Changshui Zhang

      Pages 31-41

   5. MML-Based Approach for Finite Dirichlet Mixture Estimation and Selection

      • Nizar Bouguila, Djemel Ziou

      Pages 42-51

   6. Principles of Multi-kernel Data Mining

      • Vadim Mottl, Olga Krasotkina, Oleg Seredin, Ilya Muchnik

      Pages 52-61

3. Neural Methods

   1. Comparative Analysis of Genetic Algorithm, Simulated Annealing and Cutting Angle Method for Artificial Neural Networks

      • Ranadhir Ghosh, Moumita Ghosh, John Yearwood, Adil Bagirov

      Pages 62-70

   2. Determining Regularization Parameters for Derivative Free Neural Learning

      • Ranadhir Ghosh, Moumita Ghosh, John Yearwood, Adil Bagirov

      Pages 71-79

   3. A Comprehensible SOM-Based Scoring System

      • Johan Huysmans, Bart Baesens, Jan Vanthienen

      Pages 80-89

4. Subspace Methods

   1. The Convex Subclass Method: Combinatorial Classifier Based on a Family of Convex Sets

      • Ichigaku Takigawa, Mineichi Kudo, Atsuyoshi Nakamura

      Pages 90-99

   2. SSC: Statistical Subspace Clustering

      • Laurent Candillier, Isabelle Tellier, Fabien Torre, Olivier Bousquet

      Pages 100-109

   3. Understanding Patterns with Different Subspace Classification

      • Gero Szepannek, Karsten Luebke, Claus Weihs

      Pages 110-119

5. Clustering: Basics

   1. Using Clustering to Learn Distance Functions for Supervised Similarity Assessment

      • Christoph F. Eick, Alain Rouhana, Abraham Bagherjeiran, Ricardo Vilalta

      Pages 120-131

   2. Linear Manifold Clustering

      • Robert Haralick, Rave Harpaz

      Pages 132-141

   3. Universal Clustering with Regularization in Probabilistic Space

      • Vladimir Nikulin, Alex J. Smola

      Pages 142-152

   4. Acquisition of Concept Descriptions by Conceptual Clustering

      • Silke Jänichen, Petra Perner

      Pages 153-162

6. Applications of Clustering

   1. Clustering Large Dynamic Datasets Using Exemplar Points

      • William Sia, Mihai M. Lazarescu

      Pages 163-173

   2. Birds of a Feather Surf Together: Using Clustering Methods to Improve Navigation Prediction from Internet Log Files

      • Martin Halvey, Mark T. Keane, Barry Smyth

      Pages 174-183

   3. Alarm Clustering for Intrusion Detection Systems in Computer Networks

      • Giorgio Giacinto, Roberto Perdisci, Fabio Roli

      Pages 184-193

We met again in front of the statue of Gottfried Wilhelm von Leibniz in the city of Leipzig. Leibniz, a famous son of Leipzig, planned automatic logical inference using symbolic computation, aimed to collate all human knowledge. Today, artificial intelligence deals with large amounts of data and knowledge and finds new information using machine learning and data mining. Machine learning and data mining are irreplaceable subjects and tools for the theory of pattern recognition and in applications of pattern recognition such as bioinformatics and data retrieval. This was the fourth edition of MLDM in Pattern Recognition which is the main event of Technical Committee 17 of the International Association for Pattern Recognition; it started out as a workshop and continued as a conference in 2003. Today, there are many international meetings which are titled “machine learning” and “data mining”, whose topics are text mining, knowledge discovery, and applications. This meeting from the first focused on aspects of machine learning and data mining in pattern recognition problems. We planned to reorganize classical and well-established pattern recognition paradigms from the viewpoints of machine learning and data mining. Though it was a challenging program in the late 1990s, the idea has inspired new starting points in pattern recognition and effects in other areas such as cognitive computer vision.

• classification
• computer vision
• data mining
• learning
• machine learning
• pattern mining

• Institute of Computer Vision and applied Computer Sciences, IBaI, Germany

  Petra Perner

• Institute of Media and Information Technology, Chiba University, Japan

  Atsushi Imiya

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