A Common Logic Approach to Data Mining and Pattern Recognition. - The One Clause at a Time (OCAT) Approach to Data Mining and Knowledge Discovery. - An Incremental Learning Algorithm for Inferring Logical Rules from Examples in the Framework of the Common Reasoning Process. - Discovering Rules That Govern Monotone Phenomena. - Learning Logic Formulas and Related Error Distributions. - Feature Selection for Data Mining. - Transformation of Rational Data and Set Data to Logic Data. - Data Farming: Concepts and Methods. - Rule Induction Through Discrete Support Vector Decision Trees. - Multi-Attribute Decision Trees and Decision Rules. - Knowledge Acquisition and Uncertainty in Fault Diagnosis: A Rough Sets Perspective. - Discovering Knowledge Nuggets with a Genetic Algorithm. - Diversity Mechanisms in Pitt-Style Evolutionary Classifier Systems. - Fuzzy Logic in Discovering Association Rules: An Overview. - Mining Human Interpretable Knowledge with Fuzzy Modeling Methods: An Overview. - Data Mining from Multimedia Patient Records. - Learning to Find Context Based Spelling Errors. - Induction and Inference with Fuzzy Rules for Textual Information Retrieval. - Statistical Rule Induction in the Presence of Prior Information: The Bayesian Record Linkage Problem. - Some Future Trends in Data Mining. Language: English