This is the first textbook on inductive logic programming (ILP) and
multi-relational data mining (MRDM). These subfields of data mining
and machine learning are concerned with analyzing structured data
that arise in numerous applications, such as bioinformatics, Web
mining, natural language processing, etc.
The author explains some important techniques in detail by using
case studies centered around well-known ILP or MRDM systems. These
studies are among the "classics" in the field and they
also provide a good starting point for a more general discussion.
Related systems and techniques are covered in detailed
bibliographies in each chapter.
The book addresses graduate students in computer science, databases
and artificial intelligence, as well as practitioners of data
mining and machine learning.
This book constitutes the first textbook on inductive logic
programming (ILP) and multi-relational data mining (MRDM). These
subfields of data mining and machine learning are concerned with
analyzing structured data that arise in numerous applications, such
as bioinformatics, web mining, natural language processing, etc.
The book explains some important techniques in detail by using case
studies centered around well-known ILP or MRDM systems. These case
studies are some of the 'classics' of the field and also
provide an adequate starting point for a more general discussion.
Related systems and techniques are covered in a bibliographical
section at the end of each chapter. The book addresses graduate
students in computer science, data bases and artificial
intelligence as well as practitioners of data mining and machine
learning.
From the reviews:"This book is an invaluable resource for anyone interested in exploiting the power of logical representations to learn from highly structured data. The book offers a systematic and innovative view of this important and rapidly developing area, combining technical depth and breadth of coverage. In Bristol, we use De Raedt's book as textbook for MSc students and as a reference for PhD students and researchers." (Peter A. Flach, University of Bristol) "This book provides comprehensive coverage of logical and relational learning, with an overview of inductive logic programming, multi-relational data mining, and statistical relational learning. The book is replete with examples, exercises, and case studies. The case studies use popular logical and relational systems and applications. The ample use of illustrations, tables, and bullet lists makes the book more readable and understandable. very useful to students, researchers, and practitioners in the fields of machine learning, automated knowledge discovery, data mining, and related fields." (Alexis Leon, ACM Computing Reviews, July, 2009)
Luc De Raedt is currently a full professor (C4) of computer science at the Albert-Ludwigs-University Freiburg and head of the Machine Learning lab. Before coming to Freiburg in 1999, he held positions as (parttime) senior lecturer, lecturer and assistant at the Department of Computer Science of the Katholieke Universiteit Leuven (Belgium) and as post-doc of the Fund for Scientific Research, Flanders. He obtained his undergraduate degree as well as his Ph.D. in computer science from the Katholieke Universiteit Leuven (Belgium) in 1986 and 1991. His Ph.D. thesis was subsequently published by Academic Press.
De Raedt has a rich experience in European Union research projects.´He (co-)coordinated the successful ESPRIT III and IV Inductive Logic Programming (1 and 2) projects, coordinated the IST assessment project APrIL, and the Marie Curie Training Site DAISY (Foundations of Intelligent Systems). He is at present also involved in the European IST-FET project cInQ belonging to FP5.
De Raedt has (co)-organised several international workshops and conferences.
Inhaltsangabe
Introduction. An Introduction to Logic. An Introduction to Learning and Search. Representations for Mining and Learning. Generality and Logical Entailment. The Upgrading Story. Inducing Theories. Probabilistic Logic Learning. Kernels and Distances for Structured Data. Computational Aspects of Logical and Relational Learning. Conclusions. References. Author Index. Subject Index.
