IDA-99
The Third Symposium on
Intelligent Data Analysis
Amsterdam, The Netherlands

Accepted Papers

Instructions for Authors

Papers have to be formatted following Springer-Verlag's instructions for LaTeX-submissions (follow the instructions for "Proceedings"). Please do not alter any settings int the style-file or manually insert commands that change spacing, page width or height. If your paper requires usage of additional LaTeX-packages (i.e., to include figures), restrain yourself to psfig or epsffig. If you have any questions, please contact Michael Berthold at berthold@cs.berkeley.edu prior to the deadline.

The LaTeX source files (don't forget to include any PostScript figures you are using), along with a PostScript version of the complete paper must be submitted via email to berthold@icsi.berkeley.edu. (Create a tar-archive and name it using one author's name to make identification easy.) At the same time please also fax a signed copy of Springer's copyright form to +1 (510) 643-7684, attn. M. Berthold. Please send an email containing the names of all authors along with the title of your contribution as soon as possible to the above address as well.

Due to the tight timeframe we require submission of papers by May 7th, 1999. Papers arriving later than that will not be included in the conference proceedings.

To have your paper included in the proceedings, please note that you are required to

1. Adequately address issues and concerns raised by your reviewers.
2. Format your paper using Springer-Verlag's instructions for LaTeX-submissions.
3. Register at least one author per paper before May 7th, 1999. The registration form is available.
4. Inform us who is going to present the paper at the conference by sending email to ida99@wi.leidenuniv.nl.

Accepted papers

Oral presentations:

• D. Gunetti and G. Ruffo - Intrusion Detection through Behavioral Data
• P.E. Macrossan, H.A. Abbass, K. Mengersen, M. Towsey and G. Finn - Bayesian Neural Network Learning for Prediction in the Australian Dairy Industry
• Doug Talbert and Doug Fisher - Exploiting Sample-Data Distributions to Reduce the Cost of Nearest-Neighbor Searches with KD-Trees
• David M.J. Tax, Alexander Ypma and Robert P.W. Duin - Pump Failure Detection Using Support Vector Data Descriptions
• R. Potharst and J.C. Bioch - A Decision Tree Algorithm for Ordinal Classification
• P. Sebastiani, M. Ramoni, P. Cohen, J. Warwick and J. Davis - Discovering Dynamics Using Bayesian Clustering
• L. Talavera and J. Béjar - Integrating Declarative Knowledge in Hierarchical Clustering Tasks
• Mayer Aladjem - Nonparametric Linear Discriminant Analysis by Recursive Optimization with Random Initialization
• M. Sebban and G. Richard - From Theoretical Learnability to Statistical Measures of the Learnable
• O. Cordon and F. Herrera - ALM: A Methodology for Designing Accurate Linguistic Models for Intelligent Data Analysis
• R. Nock and P. Jappy - A "Top-Down and Prune" Induction Scheme for Constrained Decision Committees
• Walter A. Kosters, Elena Marchiori and Ard Oerlemans - Mining Clusters with Association Rules
• B.J.A. Mertens and D.J. Hand - Adjusted Estimation for the Combination of Classifiers
• J. Yang, R. Parekh, V. Honavar and D. Dobbs - Data-Driven Theory Refinement Using KBDistAl
• Matthew Easley and Elizabeth Bradley - Reasoning about Input-Output Modeling of Dynamical Systems
• J. Fürnkranz - Exploiting Structural Information for Text Classification on the WWW
• Yong S. Choi and Suk I. Yoo - Multi-Agent Web Information Retrieval: Neural Network Based Approach
• D.R. Tauritz and I.G. Sprinkhuizen-Kuyper - Adaptive Information Filtering Algorithms
• A. Flexer and H. Bauer - Monitoring Human Information Processing via Intelligent Data Analysis of EEG Recordings
• G. Andrienko and N. Andrienko - Knowledge-Based Visualization to Support Spatial Data Mining
• T. Hofmann - Probabilistic Topic Maps: Navigating through Large Text Collections

• Th. Poddig and C. Huber - Data Mining for the Detection of Turning Points in Financial Time Series
• B. Yang-Stephens, M. Chales Swope, J. Locke and I. Moulinier - Computer-Assisted Classification of Legal Abstracts
• Yoshitomo Ikkai, Kazuhisa Ikeda, Norihisa Komoda, Akira Yamane and Isao Tone - Sequential Control Logic Inferring Method from Observed Plant I/O Data
• G. Cheng, K. Cho, X. Liu, G. Loizou and J.X. Wu - Evaluating an Eye Screening Test
• Jose M. Peña, Sylvain Letourneau and Fazel Famili - Application of Rough Sets Algorithms to Prediction of Aircraft Component Failure
• M.G. Kelly, D.J. Hand and N.M. Adams - Supervised Classification Problems: How to be Both Judge and Jury
• Cen Li and Gautam Biswas- Temporal Pattern Generation Using Hidden Markov Model Based Unsupervised Classification
• E. Hüllermeier - Exploiting Similarity for Supporting Data Analysis and Problem Solving
• S. Nascimento, B. Mirkin and F. Moura-Pires - Multiple Prototype Model for Fuzzy Clustering
• J. Eggermont, A.E. Eiben and J.I. van Hemert - A Comparison of Genetic Programming Variants for Data Classification
• Frank Klawonn and Annette Keller - Fuzzy Clustering Based on Modified Distance Measures
• Petko Valtchev - Building Classes in Object-Based Languages by Automatic Clustering
• S. Swift, A. Tucker and X. Liu - Evolutionary Computation to Search for Strongly Correlated Variables in High-Dimensional Time-Series
• Tapio Elomaa - The Biases of Decision Tree Pruning Strategies
• Luis Talavera - Feature Selection as Retrospective Pruning in Hierarchical Clustering
• Rosaria Silipo and Michael Berthold - Discriminative Power of Input Features in a Fuzzy Model
• Laura Firoiu and Paul Cohen - Learning Elements of Representations for Redescribing Robot Experiences
• X. Huang and F. Zhao - "Seeing" Objects in Spatial Datasets
• K. Matsumoto and Hashimoto - Intelligent Monitoring Method Using Time Varying Binomial Distribution Models for Pseudo-Periodic Communication Traffic
• R. Almond - Undoing Statistical Advice
• G. Guimaraes and A. Ultsch - A Method for Temporal Knowledge Conversion
• Nastaran Fatemi and Philippe Mulhem - A Conceptual Graph Approach for Video Data Representation and Retrieval
• Li Yang - 3D Grand Tour for Multidimensional Data and Clusters