ICDM 2023
23th Industrial Conference on Data Mining
July 12 – 16, 2023, New York, USA
www.data-mining-forum.de
Dear Authors and Participants,
Come and join us to the most exciting event on Data Mining.
We are looking forward to welcome you at our great event in New York.
Sincerely your,
Prof. Dr. Petra Perner
Chair
Petra Perner Institute of Computer Vision and Applied Computer Sciences IBaI, Germany
Program Committee
Plamen Angelov Lancaster University, United Kingdom
Antonio Dourado University of Coimbra, Portugal
Stefano Ferilli University of Bari, Italy
Warwick Graco Analytics Shed, Australia
Aleksandra Gruca Silesian University of Technology, Poland
Pedro Isaias The University of New South Wales, Australia
Piotr Jedrzejowicz Gdynia Maritime University, Poland
Martti Juhola University of Tampere, Finland
Eduardo F. Morales National Institute of Astrophysics, Optics, and Electronics, Mexico
Wieslaw Paja University of Rzeszow, Poland
Victor Sheng University of Central Arkansas, USA
Iren Todorova Valova University of Massachusetts Dartmouth, USA
Yun Zhao University of California, USA
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.
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
The Aim of the Conference
This conference is the 20th conference in a series of industrial conferences on Data Mining that will be held on yearly basis. Experts from different fields will present their applications and the results obtained by applying data mining. Besides that, newcomers in the field can get a fast introduction to Data Mining by taking the tutorial running in connection with the conference. In a problem/solution hour you will have the opportunity to present your application and ask for support by others or for cooperation in solving the problem.
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Topics of the conference
Paper submissions should be related but not limited to any of the following topics:
Applications of Data Mining in …
Marketing
Medicine
Civil Engineering
E-Commerce (Mining Logfiles)
Biotechnology
Quality Management
Multimedia Data (Image, Video, Text, Signals)
Web-Mining
Intrusion Detection in Networks
Criminology
Telecommunications
Social Sciences
Forensic Data Analysis
Drug Discovery
Agriculture
Smart Maintenance
Legal Court Cases
Energy Industries
Logistics and Supply Chain Management
Finance and Stock Markets
Meterology and more …
Theoretical and Application-oriented Topics in …
Big Data and Algorithm for Big Data
Case-Based Reasoning and Similarity-Based Reasoning
Clustering
Classification & Prediction
Statistical Learning
Association Rules
Deviation and Novelty Detection
Control Charts
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
Knowledge Management
Mining Social Media
Online Targeting & Controlling
Behavioral Targeting
Meteorological Data Mining
Data Mining in Energy Industry
Design of Experiment
Strategy of Experimentation
Capability Indices
Business Intelligence and Data Mining
Legal Informatics and Data Mining
Data Mining for Logistic and Supply Chain Management
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. Papers will appear in the conference proceedings.
Please submit your Long Paper to the CMS-System.
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.
Please submit your Short Paper and your Industry Paper to the CMS-System.
Industry Papers
We encourage industrial people to show their applications and projects for data mining. This work can be presented as poster during the poster session in the special industry track. Please submit a one page abstract including title, name and affilation.
Please submit your Short Paper and your Industry Paper to the CMS-System.
Notice that the submission is NOT the registration to the conference! Please fill out the registration form.
If you have any problem with the submission, please contact via email info@data-mining-forum.de.