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Conference Schedule and Format

The program of ECML and PKDD is as follows. The PKDD technical program runs from 3 till 5 September, and that of ECML from 5 till 7 September. On 5 September there is an overlapping day with plenary sessions. On the other days, there are two parallel tracks of technical presentations. In total 90 technical papers will be presented (selected out of about 240 submissions) at the two conferences. The program will be completed by invited talks, 11 workshops and 8 tutorials.

Invited Speakers

Tom Dietterich:
Support Vector Machines for Reinforcement Learning

Heikki Mannila:
Combining Discrete Algorithmic and Probabilistic Approaches in Data Mining

Antony Unwin:
Statistification or Mystification, the need for statistical thought in Visual Data Mining

Gerhard Widmer:
The Musical Expression Project: A Challenge for Machine Learning and Knowledge Discovery

Stefan Wrobel:
Scalability, Search and Sampling: From Smart Algorithms to Active Discovery

Regular Papers

List of Accepted Papers & Abstracts

Tutorials

Tutorials

Workshops

Workshops

Program

MONDAY


9.00	PKDD Opening
9.15	Invited Talk : Stefan Wrobel
10.15	Coffee Break
10.45	Parallel Sessions
	Session 1	Session 2
	Clustering	Meta Learning and Induction
	A data set oriented approach for clustering algorithm selection Maria Halkidi and Michalis Vazirgiannis	Fusion of Meta-Knowledge and Meta-Data for Case-Based Model Selection Melanie Hilario and Alexandros Kalousis
	A Study on the Hierarchical Data Clustering Algorithm Based on Gravity Theory Yen-Jen Oyang, Chien-Yu Chen and Tsui-Wei Yang	Data Reduction using Multiple Models Integration Aleksandar Lazarevic and Zoran Obradovic
	Automatic Construction and Refinement of a Class Hierarchy over multi-valued Data Nathalie Pernelle, Marie-Christine Rousset and Veronique Ventos	Bloomy Decision Tree for Multi-Objective Classification Einoshin Suzuki, Masafumi Gotoh and Yuta Choki
	Comparison of three objective functions for conceptual clustering Céline Robardet and Fabien Feschet	A General Measure of Rule Interestingness Szymon Jaroszewicz and Dan Simovici
12.30	Lunch Break
14.30	Session 3	Session 4
	Bio-informatics	Similarity and Distances
	Knowledge Discovery in Multi-Label Phenotype Data Amanda Clare and Ross King	Parametric Approximation Algorithms for High-Dimensional Euclidean Similarity Omer Egecioglu
	Gaphyl: A Genetic Algorithms Approach to Cladistics Clare Bates Congdon	Data Structures for Minimization of Total Within-Group Distance for Spatio-Temporal Clustering Vladimir Estivill-Castro and Michael Houle
	Biological Sequence Data Mining Yuh-Jyh Hu	Non-crisp Clustering Web Visitors by Fast,Convergent and Robust Algorithms on Access Logs Vladimir Estivill-Castro and Jianhua Yang
15.45	Coffee
16.15	Session 5	Session 6
	Logic Based Approaches	Miscellaneous
	Propositionalisation and Aggregates Arno Knobbe, Marc Haas and Arno Siebes	Self Similar Layered Hidden Markov Models Jafar Adibi and Wei-Min Shen
	Specifying Mining Algorithms with Iterative User-Defined Aggregates Fosca Giannotti, Giuseppe Manco and Franco Turini	The TwoKey Plot for Multiple Association Rules Control Antony Unwin, Heike Hofmann and Klaus Bernt
	Algorithms for the Construction of Concept Lattices and Their Diagram Graphs Sergei Kuznetsov and Sergei Obiedkov	Distinguishing natural language processes on the basis of fMRI-measured brain activation Francisco Pereira, Marcel Just and Tom Mitchell
17.30	End


TUESDAY

9.15	Invited Talk : Antony Unwin
10.15	Coffee Break
10.45	Parallel Sessions
	Session 7	Session 8
	Text Mining	Fuzzy logic and Association Rules
	Automatic Text Summarization by Text-span Extraction using Unsupervised and Semi-supervised Learning Massih-reza Amini and Patrick Gallinari	Interesting fuzzy association rules in quantitative databases Jeannette de Graaf, Walter Kosters and Jeroen Witteman
	Error Correcting Codes with Optimized Kullback-Leibler Distances for Text Categorization Joerg Kindermann, Gerhard Paass and Edda Leopold	Interestingness Measures for FuzzyAssociationRules Attila Gyenesei and Jukka Teuhola
	Sentence Filtering for Information Extraction in Genomics, a Classification Problem Claire Nedellec, Mohamed OuldAbdel Vetah and Philippe Bessieres	Implication-Based FuzzyAssociationRules Eyke Hüllermeier
	Text Categorization and Semantic Browsing with Self-Organizing Maps on non-euclidean Spaces Jörg Ontrup and Helge Ritter	Discovering Fuzzy Classification Rules with Genetic Programming and Co-Evolution Roberto Mendes, Fabricio Voznika, Alex Freitas and Julio Nievola
12.30	Lunch Break
14.30	Session 9	Session 10
	Association Rules	Time Series
	Computing Association Rules Using Partial Totals Frans Coenen, Graham Goulbourne and Paul Leng	Pattern extraction for time series classification Pierre Geurts
	Finding Association Rules that Trade Support Optimally Against Confidence Tobias Scheffer	Temporal Rule Discovery for Time-series Satellite Images and Integration with RDB Rie Honda and Osamu Konishi
	Detecting Temporal Changes in Event Sequences: An Application to Demographic Data Hendrik Blockeel, Johannes Fürnkranz, Alexia Prskawetz and Francesco Billari
15.45	Coffee
16.15	Session 11	Session 12
	Web Mining and Collaborative Filtering	Medical Applications
	Using Grammatical Inference to Automate Information Extraction from the Web Theodore W. Hong and Keith L. Clark	Discovery of Temporal Knowledge in Medical Time-Series Databases using Moving Average, Multiscale Matching and Rule Induction Shusaku Tsumoto
	Internet Document Filtering using Fourier Domain Scoring Laurence Park, Marimuthu Palaniswami and Ramamohanarao Kotagiri	Indentification of ECG Arrhythmias using Phase Space Reconstruction Felice Roberts, Richard Povinelli and Kristina Ropella
	Lightweight Collaborative Filtering Method for Binary-Encoded Data Sholom Weiss and Nitin Indurkhya	Mining Positive and Negative Knowledge in Clinical Databases based on RoughSet Model Shusaku Tsumoto
17.30	End


WEDNESDAY

9.00	Opening of the joint conference
9.30	Invited Talk : Heikki Mannila
10.30	Coffee Break
11.00	Session 13
	Learning What People (Don't) Want Thomas Hofmann
	Discovery of Temporal Patterns: Learning Rules about the Qualitative Behaviour of Time Series Frank Hoeppner
	Iterative Double Clustering for Unsupervised and Semi-supervised Learning Ran El-Yaniv and Oren Souroujon
	Discovering Admissible Simultaneous Equation Models from Observed Data Takashi Washio, Hiroshi Motoda and Yuji Niwa
12.40	Lunch Break
14.45	Invited Talk: Tom Dietterich
15.45	Coffee Break
16.15	Business Meeting
17.30	End


THURSDAY

9.15	Invited Talk : Gerhard Widmer
10.15	Coffee Break
10.45	Parallel Sessions
	Session 14	Session 15
	Reinforcement learning	Text and web mining
	Speeding up Relational Reinforcement Learning Throughthe Use of an Incremental First Order Decision Tree Algorithm Kurt Driessens, Jan Ramon and Hendrik Blockeel	Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL Peter Turney
	DQL: a New Updating Strategy for Reinforcement Learning based on Q-learning Carlos Mariano and Eduardo Morales	Text Categorization Using Transductive Boosting Hirotoshi Taira and Masahiko Haruno
	Learning While Exploring: Bridging the Gaps in the Eligibility Traces Fredrik A. Dahl and Ole Martin Halck	Second Order Features for Maximising Text Classification Performance Bhavani Raskutti, Herman Ferra and Adam Kowalczyk
	A Reinforcement Learning Algorithm Applied to Simplified Two-Player Texas Hold'em Poker Fredrik A. Dahl	Wrapping Web Information Providers by Transducer Induction Boris Chidlovskii
12.30	Lunch Break
14.30	Session 16	Session 17
	Ensemble methods I	Regression and similarity
	On the Practice of Branching Program Boosting Tapio Elomaa and Matti Kaariainen	Backpropagation in Decision Trees for Regression Victor Medina-Chico, Alberto Suarez and James F. Lutsko
	Analysis of the Performance of AdaBoost.M2 for the Simulated Digit-Recognition-Example Gunther Eibl and Karl Peter Pfeiffer	Applying the Bayesian Evidence Framework to nu-Support Vector Regression Martin Law and James Kwok
	Improving Term Extraction by System Combination using Boosting Jordi Vivaldi, Lluis Marquez and Horacio Rodriguez	A language-based similarity measure Lionel Martin and Frederic Moal
15.45	Coffee Break
16.15	Session 18	Session 19
	Ensemble methods II	Theory and optimisation
	Discovering Strong Principles of Expressive Music Performance with the PLCG Rule Learning Strategy Gerhard Widmer	Distance Correlation of Neural Network Error Surfaces: A Scalable, Continuous Optimization Problem Marcus Gallagher
	Building Committees by Clustering Models Based on Pairwise Similarity Values Thomas Ragg	Towards a Universal Theory of Artificial Intelligence based on Algorithmic Probability and Sequential Decisions Marcus Hutter
	Consensus Decision Trees: Using Consensus Hierarchical Clustering for Data Relabelling and Reduction Branko Kavsek, Nada Lavrac and Anuska Ferligoj	Convergence and Error Bounds for Universal Prediction of Nonbinary Sequences Marcus Hutter
	Using Diversity in Preparing Ensembles of Classifiers Based on Different Feature Subsets to Minimize Generalization Error Gabriele Zenobi and Padraig Cunningham
18.00	End


FRIDAY

9.00	Session 20	Session 21
	Probabilistic approaches	Evolutionary approaches
	Proportional k-Interval Discretization for Naive-Bayes Classifiers Ying Yang and Geoffrey I. Webb	Symbolic Discriminant Analysis for Mining Gene Expression Patterns Jason Moore, Joel Parker and Lance Hahn
	Geometric Properties of Naive Bayes in Nominal Domains Huajie Zhang and Charles Ling	An evolutionary algorithm for cost-sensitive decision rule learning Wojciech Kwedlo and Marek Kretowski
	Understanding Probabilistic Classifiers Ashutosh Garg and Dan Roth	Improving the robustness and encoding complexity of Behavioural clones Rui Camacho and Pavel Brazdil
10.15	Coffee Break
10.45	Session 22	Session 23
	Inductive logic programming	Agents, discovery, and qualitative modelling
	Using Different Refinement Operators in Inductive Logic Programming for Semantic Parsing Lappoon Tang and Raymond Mooney	Social Agents Playing a Periodical Policy Ann Nowe, Johan Parent and Katja Verbeeck
	An Axiomatic Approach to Feature Term Generalization Hassan Ait-Kaci and Yutaka Sasaki	Learning when to collaborate among learning agents Santiago Ontaon and Enric Plaza
	Lazy Induction of Descriptions for Relational case-based learning Eva Armengol and Enric Plaza	Using Domain Knowledge on Population Dynamics Modeling for Equation Discovery Ljupco Todorovski and Saso Dzeroski
	A Framework for Learning Rules from Multiple Instance Data Yann Chevaleyre and Jean-Daniel Zucker	Induction of Qualitative Trees Dorian Suc and Ivan Bratko
12.30	Lunch Break
14.30	Session 24	Session 25
	Statistical approaches I	Classification I
	Comparing the Bayes and typicalness frameworks Thomas Melluish, Craig Saunders, Ilia Nouretdinov and Volodya Vovk	The Evaluation of Predictive Learners Kevin Korb, Lucas Hope and Michelle Hughes
	Importance Sampling Techniques in Neural Detector Training Jose L. Sanz-Gonzalez and Diego Andina	A Unified Framework For Evaluation Metrics In Classification Using Decision Trees Ricardo Vilalta, Daniel Oblinger, Mark Brodie and Irina Rish
	Efficiently Determine the Starting Sample Size for Progressive Sampling Baohua Gu, Bing Liu, Feifang Hu and Huan Liu	Estimating the predictive accuracy of a classifier Hilan Bensusan and Alexandros Kalousis
15.45	Coffee Break
16.15	Session 26	Session 27
	Statistical approaches II	Classification II
	A Mixture Approach to Novelty Detection Using Training Data with Outliers Martin Lauer	Using subclasses to improve classification learning Achim Hoffmann, Rex Kwok and Paul Compton
	Extraction of Recurrent Patterns from Stratified Ordered Trees Jean-Gabriel Ganascia	A Simple Approach to Ordinal Classification Eibe Frank and Mark Hall
	Learning of Variability for Invariant Statistical Pattern Recognition Daniel Keysers, Wolfgang Macherey, Joerg Dahmen and Hermann Ney	Classification on data with biased class distribution Slobodan Vucetic and Zoran Obradovic
17.30	End