Knowledge Discovery in Databases: PKDD 2005: 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, Porto, Portugal, October 3-7, 2005, Proceedings

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Alípio Jorge, Luís Torgo, Pavel Brazdil, Rui Camacho, João Gama
Springer Science & Business Media, 2005 M09 26 - 719 pages
The European Conference on Machine Learning (ECML) and the European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD) were jointly organized this year for the ?fth time in a row, after some years of mutual independence before. After Freiburg (2001), Helsinki (2002), Cavtat (2003) and Pisa (2004), Porto received the 16th edition of ECML and the 9th PKDD in October 3–7. Having the two conferences together seems to be working well: 585 di?erent paper submissions were received for both events, which maintains the high s- mission standard of last year. Of these, 335 were submitted to ECML only, 220 to PKDD only and 30 to both. Such a high volume of scienti?c work required a tremendous e?ort from Area Chairs, Program Committee members and some additional reviewers. On average, PC members had 10 papers to evaluate, and Area Chairs had 25 papers to decide upon. We managed to have 3 highly qua- ?edindependentreviewsperpaper(withveryfewexceptions)andoneadditional overall input from one of the Area Chairs. After the authors’ responses and the online discussions for many of the papers, we arrived at the ?nal selection of 40 regular papers for ECML and 35 for PKDD. Besides these, 32 others were accepted as short papers for ECML and 35 for PKDD. This represents a joint acceptance rate of around 13% for regular papers and 25% overall. We thank all involved for all the e?ort with reviewing and selection of papers. Besidesthecoretechnicalprogram,ECMLandPKDDhad6invitedspeakers, 10 workshops, 8 tutorials and a Knowledge Discovery Challenge.

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Contents

Invited Talks
1
Introducing Softness in Constrained
22
Tree2 Decision Trees for Tree Structured Data
46
Theoretical
59
Ensembles of Balanced Nested Dichotomies for Multiclass Problems
84
An Adaptive Nearest Neighbor Classification Algorithm for Data
108
Support Vector Random Fields for Spatial Classification
121
A Correspondence Between Maximal Complete Bipartite Subgraphs
146
The Relation of Closed Itemset Mining Complete Pruning Strategies
437
Evaluating the Correlation Between Objective Rule Interestingness
453
CorpusBased Neural Network Method for Explaining Unknown Words
470
Producing Accurate Interpretable Clusters from HighDimensional
486
Rank Measures for Ordering
503
FrequencyBased Separation of Climate Signals
519
Feature Extraction from Mass Spectra for Classification of Pathological
536
Testing Theories in Particle Physics Using Maximum Likelihood
552

Mining Model Trees from Spatial Data
169
Mining Paraphrases from Selfanchored Web Sentence Fragments
193
A Systematic Comparison of FeatureRich Probabilistic Classifiers
217
Unsupervised Discretization Using TreeBased Density Estimation
240
Nonstationary Environment Compensation Using Sequential
264
Characterization of Novel HIV Drug Resistance Mutations Using
285
A WSRFEnabled Weka Toolkit for Distributed Data
309
Locally Linear Embedding with Geodesic Distance
331
Active Sampling for Knowledge Discovery from Biomedical Data
343
Fast Burst Correlation of Financial Data
368
A Quantitative Comparison of the Subgraph Miners MoFa gSpan
392
A Probabilistic ClusteringProjection Model for Discrete Data
417
Clustering and Prediction of Mobile User Routes from Cellular
569
Elastic Partial Matching of Time Series
577
Visual Terrain Analysis of HighDimensional Datasets
593
A Comparison Between Block CEM and TwoWay CEM Algorithms
609
Improvements in the Data Partitioning Approach for Frequent Itemsets
625
A Biclustering Framework for Categorical Data
643
Indexed Bit Map IBM for Mining Frequent Sequences
659
Speeding Up Logistic Model Tree Induction
675
A Random Method for Quantifying Changing Distributions in Data
684
An Incremental Algorithm for Mining Generators Representation
701
Author Index
717
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