Proceedings Book

Editorial

The eighteenth event of the International Conference on Machine Learning and Data Mining MLDM was held in New York (www.mldm.de) running under the umbrella of the World Congress “The Frontiers in Intelligent Data and Signal Analysis, DSA2022” (www.worldcongressdsa.com). At a time when we are still struggling with the corona pandemic, we scientists from different nations have gathered together for a peaceful discourse on an important research focus in the field of data mining and machine learning. With our conference, we scientists show that we respect the opinions and work of others. That we are ready to consider them peacefully and in friendship under the critical view of the high scientific standards that this conference has. The International Program Committee has done an excellent and time-consuming job to select the best papers and provide important guidance on the work of the authors. I would like to thank all the members of the Program Committee for their efforts and that you have contributed with your top-class scientific competence. The best papers are presented at this conference. The acceptance rate is 33%. Thank you to all the scientists who have participated in this conference with your excellent work. A special issue will be done after the conference in the Intern. Journal Transactions on Machine Learning and Data Mining (http://www.ibaipublishing.org/journal/mldm/about.php). I would also like to thank those scientists who have participated in the conference with their work and have not been successful. Even if we have rejected work, we hope that the indications of the program committee will encourage you to reconsider your work and that you will perhaps face the critical scientific consideration of your work by the international program committee again next year. The tutorial days rounded up the high quality of the conference. Researchers and practitioners got an excellent insight in the research and technology of the respective fields, the new trends and the open research problems that we like to study further. A tutorial on Data Mining and a tutorial on Case-Based Reasoning, were held after the conference. I also thank the members of the Institute of Computer Vision and applied Computer Sciences, Germany (www.ibai-institut.de), who handled the conference as secretariat. We appreciate the help and understanding of the editorial staff at ibai-publishing house, who supported the publication of these proceedings (http://www.ibaipublishing.org/html/proceeding.php). Last, but not least, we wish to thank all the speakers and participants who contributed to the success of the conference. We hope to see you in 2023 in New York again at the next World Congress on “The Frontiers in Intelligent Data and Signal Analysis, DSA 2023” (www.worldcongressdsa.com), which combines under its roof the following three events: International Conferences Machine Learning and Data Mining, MLDM (www.mldm.de), the Industrial Conference on Data Mining, ICDM (www.data-mining-forum.de, and the International Conference on Mass Data Analy- sis of Signals and Images in Medicine, Biotechnology, Chemistry, Biometry, Security, Agriculture, Drug Discovery and Food Industry, MDA (www.mda-signals.de), the workshops and tutorials. July 2022 Petra Perner

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