Proceedings, Machine Learning and Data Mining in Pattern Recognition, Petra Perner (Ed.), 17th International Conference on Machine Learning and Data Mining, MLDM 2021, New York, USA, July 18-22, 2021, ibai-publishing, P-ISSN 1864-9734 E-ISSN 2699-5220 ISBN 978-3-942952-81-1."/

Proceedings Book


Machine Learning and Data Mining in Pattern Recognition


Petra Perner (Ed.)

17th International Conference on Machine Learning and Data Mining
MLDM 2021
New York, USA, July 18-22, 2021, ibai-publishing, P-ISSN 1864-9734 E-ISSN 2699-5220, ISBN 978-3-942952-81-1

www.mldm.de


ibai publishing house

Open Access Proceedings Book MLDM 2021


Abstract

The seventeenth event of the International Conference on Machine Learning and Data Mining MLDM 2021 was held in New York (www.mldm.de) running under the um-brella of the World Congress “The Frontiers in Intelligent Data and Signal Analysis, DSA2021” (www.worldcongressdsa.com). After the peer-review process, we accepted nineteen high-quality papers for oral presentation. Seventeen papers are published in the proceedings book. http://www.ibai-publishing.org/html/proceedings_2021/proceedings_mldm_2021.php The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data-mining methods for the different multimedia data types such as image mining, text mining, video mining, and Web mining. Extended versions of selected papers will appear in the international journal Transactions on Machine Learning and Data Mining (www.ibai-publishing.org/journal/mldm). 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, a tutorial on Case-Based Reasoning, a tutorial on Intel-ligent Image Interpretation and Computer Vision in Medicine, Biotechnology, Chem-istry and Food Industry, and a tutorial on Standardization in Immunofluorescence were held before the conference. We would like to thank all reviewers for their highly professional work and their effort in reviewing the papers. We would also thank the members of the FutureLab Artificial Intelligence IBaI-2 (www.futurelab-ai-ibai-2.de), who handled the confer-ence 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.ibai publishing.org/html/proceeding.php). Last, but not least, we wish to thank all the speakers and participants who contrib-uted to the success of the conference. See you in 2022 in New York at the next World Congress (www.worldcongressdsa.com) on “The Frontiers in Intelligent Data and Signal Analysis, DSA2022”, which combines under its roof the following three events: International Conferences on 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 Analysis of Signals and Images in Medicine, Biometry, Drug Discovery Biotechnology, Chemistry and Food Industry, MDA. July 2021 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