Knowledge Discovery in Databases: PKDD 2005: 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, Porto, Portugal, October 3-7, 2005, ProceedingsAlípio 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. |
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 |
717 | |
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Common terms and phrases
accuracy algorithm analysis applied approach associated attributes average better block classification closed clustering combination compared complexity computed Conference consider constraints contains corresponding cost data mining database dataset decision tree defined described different discovery distance distribution documents effect estimate evaluation example experiments extracted Figure first fragments frequent function given graph improve increase instances interesting International Italy itemsets iterations knowledge labels learning Machine matrix means measure method node objects obtained optimal pairs parameters partition patterns performance points positive possible prediction present probability problem Proceedings produce properties proposed query random rank References relation represent representation respectively rules samples selection sequence sequential shown shows similar solution space spatial step structure subset Table task technique threshold tree vector weight