Data Mining and Knowledge Discovery via Logic-Based Methods: Theory, Algorithms, and Applications

Algorithmic Issues.- Inferring a Boolean Function from Positive and Negative Examples.- A Revised Branch-and-Bound Approach for Inferring a Boolean Function from Examples.- Some Fast Heuristics for Inferring a Boolean Function from Examples.- An Approach to Guided Learning of Boolean Functions.- An Incremental Learning Algorithm for Inferring Boolean Functions.- A Duality Relationship Between Boolean Functions in CNF and DNF Derivable from the Same Training Examples.- The Rejectability Graph of Two Sets of Examples.- Application Issues.- The Reliability Issue in Data Mining: The Case of Computer-Aided Breast Cancer Diagnosis.- Data Mining and Knowledge Discovery by Means of Monotone Boolean Functions.- Some Application Issues of Monotone Boolean Functions.- Mining of Association Rules.- Data Mining of Text Documents.- First Case Study: Predicting Muscle Fatigue from EMG Signals.- Second Case Study: Inference of Diagnostic Rules for Breast Cancer.- A Fuzzy Logic Approach to Attribute Formalization: Analysis of Lobulation for Breast Cancer Diagnosis.- Conclusions.