The Aim of the
Conference
The aim of the conference is to bring together researchers from all over the
world who deal with machine learning and data mining in order to discuss the
recent status of the research and to direct further developments. Basic
research papers as well as application papers are welcome.
Topics of the conference
All kinds of applications are welcome but special preference will be given to
multimedia related applications, applications from live sciences and
webmining.
Paper submissions should be related but not limited to any of the following
topics:
association rules
case-based reasoning and learning
classification and interpretation of images, text, video
conceptional learning and clustering
Goodness measures and evaluaion (e.g. false discovery rates)
inductive learning including decision tree and rule induction learning
knowledge extraction from text, video, signals and images
mining gene data bases and biological data bases
mining images, temporal-spatial data, images from remote sensing
mining structural representations such as log files, text documents and HTML
documents
mining text documents
organisational learning and evolutional learning
probabilistic information retrieval
Sampling methods
Selection with small samples
similarity measures and learning of similarity
statistical learning and neural net based learning
video mining
visualization and data mining
Applications of Clustering
Aspects of Data Mining
Applications in Medicine
Autoamtic Semantic Annotation of Media Content
Bayesian Models and Methods
Case-Based Reasoning and Associative Memory
Classification and Model Estimation
Content-Based Image Retrieval
Decision Trees
Deviation and Novelty Detection
Feature Grouping, Discretization, Selection and Transformation
Feature Learning
Frequent Pattern Mining
High-Content Analysis of Microscopic Images in Medicine, Biotechnology and
Chemistry
Learning and adaptive control
Learning/adaption of recognition and perception
Learning for Handwriting Recognition
Learning in Image Pre-Processing and Segmentation
Learning in process automation
Learning of internal representations and models
Learning of appropriate behaviour
Learning of action patterns
Learning of Ontologies
Learning of Semantic Inferencing Rules
Learning of Visual Ontologies
Learning robots
Mining Images in Computer Vision
Mining Images and Texture
Mining Motion from Sequence
Neural Methods
Network Analysis and Intrusion Detection
Nonlinear Function Learning and Neural Net Based Learning
Real-Time Event Learning and Detection
Retrieval Methods
Rule Induction and Grammars
Speech Analysis
Statistical and Conceptual Clustering Methods
Statistical and Evolutionary Learning
Subspace Methods
Support Vector Machines
Symbolic Learning and Neural Networks in Document Processing
Time Series and Sequential Pattern Mining
Audio Mining
Cognition and Computer Vision
Clustering
Classification & Prediction
Statistical Learning
Association Rules
Telecommunication
Design of Experiment
Strategy of Experimentation
Capability Indices
Deviation and Novelty Detection
Control Charts
Design of Experiments
Capability Indices
Conceptional Learning
Goodness Measures and Evaluation (e.g. false discovery rates)
Inductive Learning Including Decision Tree and Rule Induction Learning
Organisational Learning and Evolutional Learning
Sampling Methods
Similarity Measures and Learning of Similarity
Statistical Learning and Neural Net Based Learning
Visualization and Data Mining
Deviation and Novelty Detection
Feature Grouping, Discretization, Selection and Transformation
Feature Learning
Frequent Pattern Mining
Learning and Adaptive Control
Learning/Adaption of Recognition and Perception
Learning for Handwriting Recognition
Learning in Image Pre-Processing and Segmentation
Mining Financial or Stockmarket Data
Mining Motion from Sequence
Subspace Methods
Support Vector Machines
Time Series and Sequential Pattern Mining
Desirabilities
Graph Mining
Agent Data Mining
Applications in Software Testing
Authors can submit their paper in long or short version.
Long Paper
The paper must be formatted in the Springer LNCS format. They should have at
most 15 pages. The papers will be reviewed by the program committee. Accepted
long papers will be published by Springer Verlag in the LNAI Series in the book
Advances in Data Mining, edited by Petra Perner.
Short Paper
Short papers are also welcome and can be used to describe work in progress or
project ideas. They can have 5 to max. 15 pages, formatted in Springer LNCS
format. Accepted short papers will be presented as poster in the poster
session. They will be published in a special poster proceedings book.
Program Committee
Chair
Petra Perner IBaI Leipzig, Germany
Committee
Sergey Ablameyko Belarus State University, Belarus
Reneta Barneva The State University of New York at Fredonia, USA
Michelangelo Ceci Universtiy of Bari, Italy
Patrick Bouthemy INRIA VISTA, France
Xiaoqing Ding Tsinghua University, China
Christoph F. Eick Universtiy of Houston, USA
Ana Fred Technical University of Lisboa, Portugal
Giorgio Giacinto University of Cagliari, Italy
Makato Haraguchi Hokkaido University of Sapporo, Japan
Dimitris Karras Chalkis Institute of Technology, Greece
Adam Krzyzak Concordia University, Canada
Thang V. Pham University of Amsterdam, The Netherlands
Linda Shapiro University of Washington, USA
Tamas Sziranyi MTA-SZTAKI, Hungary
Francis E.H. Tay National University of Singapore, Singapore
Alexander Ulanov HP Labs, Russia
Zeev Volkovich ORT Braude College of Engineering, Israel
Patrick Wang Northeastern University, USA
An industrial exhibition running in connection with the conference will give
you the opportunity to look at new trends and systems in industry and to
present your research to industry.
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