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Computer Science - Software Engineering | Logical and Relational Learning

Logical and Relational Learning

De Raedt, Luc

2008, XV, 387 p.

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  • First textbook on multirelational data mining and inductive logic programming

This textbook covers logical and relational learning in depth, and hence provides an introduction to inductive logic programming (ILP), multirelational data mining (MRDM) and (statistical) relational learning (SRL). These subfields of data mining and machine learning are concerned with the analysis of complex and structured data sets that arise in numerous applications, such as bio- and chemoinformatics, network analysis, Web mining, natural language processing, within the rich representations offered by relational databases and computational logic.

The author introduces the machine learning and representational foundations of the field and explains some important techniques in detail by using some of the classic case studies centered around well-known logical and relational systems.

The book is suitable for use in graduate courses and should be of interest to graduate students and researchers in computer science, databases and artificial intelligence, as well as practitioners of data mining and machine learning. It contains numerous figures and exercises, and slides are available for many chapters.

Content Level » Graduate

Keywords » artificial intelligence - data mining - database - intelligence - learning - machine learning - programming - relational database - web mining

Related subjects » Artificial Intelligence - Database Management & Information Retrieval - Software Engineering

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