Proceedings Book

Abstract

The pandemic "Corona" has put us this year before a difficult time. ...

The best of the best of us are represented with their papers in this volume. They presented themselves personal or in online presentations in the conference. The acceptance rate for the submitted paper of our conference was 33% percent for long paper as well as short papers. Because of many refusals because of missing financial means or other reasons the acceptance rate decreased to few percent. This shows once more the excellent quality of these scientists. Their papers are of most excellent quality and expand the state-of-the-art in an excellent way.

This proceeding volume present theoretical work on known topics such as clustering, classification and prediction, graph mining but also application-oriented theoretical work for different purposes e.g. based deep learning and others.

One invited talk was given by Prof. Dan A. Simovici on a theoretical subject on “Information theoretic approaches in data mining”. His paper is also included in the proceedings.

The proceedings will be freely accessible as an OPEN-ACCESS Proceedings of a wide public so that, the new acquired knowledge on the different subjects is able to spread around quickly worldwide. You can find the proceedings at http://www.ibai-publishing.org/html/proceeding2020.php.
In this time, flexibility was a must Because the situation in the USA was still diffi-cult, we have moved the conference to Amsterdam in the Netherlands. Here a variety of the participants was able to do outward journeys. The ones who could not travel, were online present.

Extended versions of selected papers will appear in the international journal Trans-actions on Machine Learning and Data Mining (www.ibai-publishing.org/journal/mldm). We invite you to join us in 2021 in New York again to the next International Con-ference on Machine Learning and Data Mining MLDM. The conference will run again under the umbrella of the Worldcongress (www.worldcongressdsa.com) “The Frontiers in Intelligent Data and Signal Analysis, DSA2020” that combines under his roof the following three events: International Conferences Machine Learning and Data Mining MLDM, the Industrial Conference on Data Mining ICDM , and the International Conference on Mass Data Analysis of Signals and Images in Artificial Intelligence and Pattern Recognition with Application in with Applications in Medi-cine, r/g/b Biotechnology, Food Industries and Dietetics, Biometry and Security, Ag-riculture, Drug Discover, and System Biology MDA-AI&PR.

We will give then the tutorials on Data Mining, Case-Based Reasoning, and Intelligent Image Analysis again (http://www.data-mining-forum.de/tutorials.php) again. The workshops running in connection with ICDM will also be given (http://www.data-mining-forum.de/workshops.php).

We would warmly invite you with pleasure to contribute to this conference. Please come and join us. We are awaiting you.

Petra Perner, July 2020

Keywords: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