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This book presents a logic‑based framework for data mining and knowledge discovery, focusing on approaches that infer Boolean functions (rules) from positive and negative examples. It develops theoretical and algorithmic foundations, including branch‑and‑bound, heuristic, incremental learning, and monotone Boolean methods, and illustrates them with real‑world applications — from medical diagnosis to signal processing. By combining mathematical rigor with practical case studies, the book provides a distinctive perspective on how logical and combinatorial methods can address classification, rule induction, and pattern recognition problems, making it a valuable resource for researchers and practitioners in data mining, machine learning, and knowledge-based systems

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245: $a: Data Mining and Knowledge Discovery via Logic‑Based Methods
245: $b:
082: $a:
100: $a: Evangelos Triantaphyllou
650: $a: Computer Science
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440: $v:
250: $b: Springer
250: $c: 2010
020: $a: 978‑1‑4614‑2613‑4
300: $a: 350