20th Industrial Conference on Data Mining ICDM 2020

The Aim of the Conference This conference is the thirteen 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. « top 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 Simil... Read more

The Aim of the Conference This conference is the thirteen 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. « top 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 (e.g. false discovery rates) Inductive Learning Including Decision Tree and Rule Induction Learning