Advances in Intelligent Data Analysis, IDA 1999

Learning.- From Theoretical Learnability to Statistical Measures of the Learnable.- ALM: A Methodology for Designing Accurate Linguistic Models for Intelligent Data Analysis.- A "Top-Down and Prune" Induction Scheme for Constrained Decision Committees.- Mining Clusters with Association Rules.- Evolutionary Computation to Search for Strongly Correlated Variables in High-Dimensional Time-Series.- The Biases of Decision Tree Pruning Strategies.- Feature Selection as Retrospective Pruning in Hierarchical Clustering.- Discriminative Power of Input Features in a Fuzzy Model.- Learning Elements of Representations for Redescribing Robot Experiences.- "Seeing" Objects in Spatial Datasets.- Intelligent Monitoring Method Using Time Varying Binomial Distribution Models for Pseudo-Periodic Communication Traffic.- Visualization.- Monitoring Human Information Processing via Intelligent Data Analysis of EEG Recordings.- Knowledge-Based Visualization to Support Spatial Data Mining.- Probabilistic Topic Maps: Navigating through Large Text Collections.- 3D Grand Tour for Multidimensional Data and Clusters.- Classification and Clustering.- A Decision Tree Algorithm for Ordinal Classification.- Discovering Dynamics Using Bayesian Clustering.- Integrating Declarative Knowledge in Hierarchical Clustering Tasks.- Nonparametric Linear Discriminant Analysis by Recursive Optimization with Random Initialization.- Supervised Classification Problems: How to Be Both Judge and Jury.- Temporal Pattern Generation Using Hidden Markov Model Based Unsupervised Classification.- Exploiting Similarity for Supporting Data Analysis and Problem Solving.- Multiple Prototype Model for Fuzzy Clustering.- A Comparison of Genetic Programming Variants for Data Classification.- Fuzzy Clustering Based onModified Distance Measures.- Building Classes in Object-Based Languages by Automatic Clustering.- Integration.- Adjusted Estimation for the Combination of Classifiers.- Data-Driven Theory Refinement Using KBDistAl.-

David J. Hand, Professor em. Für Mathematik und Senior Research Investigator am Imperial College London, ist ehemaliger Präsident der Royal Statistical Society und derzeit wissenschaftlicher Chefberater von Winston Capital Management, einem der erfolgreichsten auf automatisierten (algorithmischen) Handel spezialisierten Hedgefonds in Europa. David Hand lebt in London.