This first textbook on multi-relational data mining and inductive logic programming provides a complete overview of the field. It is self-contained and easily accessible for graduate students and practitioners of data mining and machine learning.
Format: Hardback
CONTRIBUTORS: Luc De Raedt
EAN: 9783540200406
COUNTRY: Germany
PAGES:
WEIGHT: 770 g
HEIGHT: 235 cm
PUBLISHED BY: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
DATE PUBLISHED: 2008-09-12
CITY:
GENRE: COMPUTERS / Artificial Intelligence / General, COMPUTERS / Database Administration & Management, COMPUTERS / Data Science / Data Analytics, COMPUTERS / System Administration / Storage & Retrieval, COMPUTERS / Software Development & Engineering / General
WIDTH: 155 cm
SPINE:
Book Themes:
Software Engineering, Databases, Data warehousing, Data mining, Information retrieval, Applied computing, Artificial intelligence, Expert systems / knowledge-based systems
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)
