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Computer Science - Database Management & Information Retrieval | Foundations of Rule Learning

Foundations of Rule Learning

Fürnkranz, Johannes, Gamberger, Dragan, Lavrač, Nada

2012, XVIII, 334 p.

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  • Fills a significant gap in the machine learning literature
  • Explains the most comprehensive knowledge representation formalism
  • Offers researchers and graduate students a clear unifying terminology

Rules – the clearest, most explored and best understood form of knowledge representation – are particularly important for data mining, as they offer the best tradeoff between human and machine understandability. This book presents the fundamentals of rule learning as investigated in classical machine learning and modern data mining. It introduces a feature-based view, as a unifying framework for propositional and relational rule learning, thus bridging the gap between attribute-value learning and inductive logic programming, and providing complete coverage of most important elements of rule learning.

The book can be used as a textbook for teaching machine learning, as well as a comprehensive reference to research in the field of inductive rule learning. As such, it targets students, researchers and developers of rule learning algorithms, presenting the fundamental rule learning concepts in sufficient breadth and depth to enable the reader to understand, develop and apply rule learning techniques to real-world data.

Content Level » Research

Keywords » Association rule learning - Classification rule induction - Propositional rule learning - Relational data mining - Subgroup discovery

Related subjects » Artificial Intelligence - Database Management & Information Retrieval - Image Processing - Physical & Information Science - Theoretical Computer Science

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