Discovery science

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This comprehensive collection features invited papers and regular contributions that explore various innovative approaches in artificial intelligence and data mining. Topics include algorithms for collaborative discovery from distributed information sources, training support vector machines, and projects like the Robot Scientist and Arrowsmith. The long papers delve into practical algorithms for pattern-based linear regression, named entity recognition for the Indonesian language, and bias management in Bayesian network classifiers. Further studies analyze literature-based discoveries, cross-language mining for acronyms, and frequent pattern mining in large databases. The work also covers movement analysis of Medaka fish using decision trees, support vector inductive logic programming, and measuring over-generalization in biosequences. Regular papers discuss automatic extraction of proteins from biological texts, evaluating interestingness measures, and distributed clustering using probabilistic models. Project reports highlight self-generation of control rules for water treatment, semantic enrichment of data tables for food risk assessment, and knowledge discovery through visualization techniques. The collection emphasizes advancements in machine learning, classification methods, and text mining, showcasing the intersection of AI with various scientific domains, particularly in biomedical research and data analysis.