ECML/PKDD 2004, Pisa, Italy, September 20-24, 2004
News
webmaster contact at ecmlpkddweb@isti.cnr.it
Photo by lenzo.it

Program at a glance

  Sun 19 Mon 20 Tue 21 Wed 22 Thu 23 Fri 24  
9.00   Challenge, workshops W1, W2, W3, W4, W5, tutorials Invited talk: Hand Invited talk: Achlioptas Invited talk: Domingos Invited talk: Agrawal 9.00
9.30 9.30
10.00 Best ECML student paper Best PKDD paper Best ECML paper Coffee break 10.00
10.30 Coffee break Coffee break Coffee break Coffee break Coffee break Workshops W6, W7, W8, W9, W10 10.30
11.00 KDNet Board Challenge, workshops W1, W2, W3, W4, W5, tutorials RL1
EM
AL
IA1
RL2
CL
AE
IA2
DT
11.00
11.30 11.30
12.00 12.00
12.30 Lunch Lunch 12.30
13.00 Lunch Lunch Lunch 13.00
13.30 Lunch 13.30
14.00 KDNet Board Challenge, Workshops W1, W2, W3, W4, W5, tutorials 14.00
14.30 GR
BC
MM
RD
TM2
KM1
AR
KM2
MI
14.30
15.00 Workshops W6, W7, W8, W9, W10, tutorials 15.00
15.30 Coffee break Coffee break 15.30
16.00 KDNet Board Challenge, Workshops W1, W2, W3, W4, W5, tutorials Coffee break Coffee break Coffee break 16.00
16.30 TM1
CLU1
CLR
SM
FS
CLU2
Coffee break 16.30
17.00 Workshops W6, W7, W8, W9, W10, tutorials 17.00
17.30   Challenge 17.30
18.00 Best PKDD student paper Poster session 18.00
18.30   KDNet project exhibit Community meeting   18.30
19.00 19.00
19.30     19.30
20.00 Invited talk: Chakrabarti 20.00
20.30 20.30
21.00 21.00

legend

  Presentations   Invited talks Social events   Lunch   Coffee break

Tutorials

  • T1 - Evaluation in Web Mining
  • T2 - Symbolic Data Analysis
  • T3 - Distributed Data Mining for Sensor Networks
  • T4 - Radial Basis Functions: An Algebraic Approach (with Data Mining Applications)
  • T5 - Mining Unstructured Data
  • T6 - Statistical Approaches used in Machine Learning
  • T7 - Rule-based Data Mining Methods for Classification Problems in the Biomedical Domain

Workshops

  • W1 - Statistical Approaches for Web Mining (SAWM)
  • W2 - Symbolic and Spatial Data Analysis: Mining Complex Data Structures
  • W3 - Third International Workshop on Knowledge Discovery in Inductive Databases (KDID)
  • W4 - Data Mining and Adaptive Modelling Methods for Economics and Management (IWAMEM-04)
  • W5 - Privacy and Security Issues in Data Mining
  • W6 - Knowledge Discovery and Ontologies
  • W7 - Mining Graphs, Trees and Sequences (MGTS'04)
  • W8 - Advances in Inductive Rule Learning
  • W9 - Data Mining and Text Mining for Bioinformatics
  • W10 - Knowledge Discovery in Data Streams

Topics

  • RL1 - Reinforcement Learning 1
  • AL - Algorithms
  • EM - Ensemble Methods
  • GR - Graphs
  • BC - Bayesian Classification
  • MM - E-Mail Mining
  • TM1 - Text Mining and Learning from Text 1
  • CL1 - Clustering 1
  • CLR - Classification and Regression
  • RL2 - Reinforcement Learning 2
  • IA1 - Innovative Applications 1
  • CL - Classification
  • RD - Rule Discovery
  • TM2 - Text Mining and Learning from Text 2
  • KM1 - Kernel Methods and Support Vector Machines 1
  • SM - Spatial Data Mining
  • FS - Feature Selection
  • CLU2 - Clustering 2
  • IA2 - Innovative Applications 2
  • AE - Algorithms and Environments
  • DT - Decision Trees
  • AR - Association Rules
  • KM2 - Kernel Methods and Support Vector Machines 2
  • ML - Meta-Learning and Case-Based Reasoning