The Industrial Conference on Data Mining ICDM () is held on yearly basis. Researchers from all over the world will present theoretical and application-oriented topics on Data Mining. Practicioners can present and discuss their ongoing projects in Industry Sessions. Industrial Exhibition · Best-Paper-Award for Talks and Posters  Workshops: Case-Based Reasoning CBR-MD, DM in Marketing DMM, and DM in Agriculture DMA The Industrial Conference of Data Mining, ICDM, is one of the leading events where researchers, practicioners and newcomers in the field of data mining come together to share their ideas and discuss new trends in Data Mining. At this conference series, quite a number of new theoretical and practical topics in data mining have been developed over the years. Work on multimedia-data mining and applications in medicine, agriculture as well as in security have been presented. The idea of presenting industrial papers has attracted a lot of practitioners from all over the world and from one year to another more and more people have been sharing the main ideas of their projects with us and have been getting valuable feedback from the research auditorium, thus drawing the interest of researchers to important aspects of practical work. The conference has achieved its aim to bridge research with practice and has led to a fruitful platform for the development of the data-mining field for researchers and practitioners.

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
• E-Commerce (Mining Logfiles)
• Biotechnology
• Quality Management
• Multimedia Data (Image, Video, Text, Signals)
• Web-Mining
• Intrusion Detection in Networks
• Agriculture
• Meterology

Theoretical and Application-oriented Topics in ...

• • Case-Based Reasoning and Similarity-Based Reasoning
  • Clustering
  • Classification & Prediction
  • Statistical Learning
  • Association Rules
  • Telecommunication
  • Design of Experiment
  • Strategy of Experimentation
  • Capability Indices
  • Deviation and Novelty Detection
  • Control Charts
  • Conceptional Learning
  • Goodness Measures and Evaluation ( 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 Logistics
  • Data Mining in Energy Industry
  • Business Intelligence and Data Mining
  • Big Data and Algorithm for Big Data
  • Legal Informatics and Data Mining
  • Data Mining for Logistic and Supply Chain Management