Relational Knowledge Discovery
1st Edition
Published on 14. June 2012
978-1-139-51187-2 (ISBN)
Description
What is knowledge and how is it represented? This book focuses on the idea of formalising knowledge as relations, interpreting knowledge represented in databases or logic programs as relational data and discovering new knowledge by identifying hidden and defining new relations. After a brief introduction to representational issues, the author develops a relational language for abstract machine learning problems. He then uses this language to discuss traditional methods such as clustering and decision tree induction, before moving onto two previously underestimated topics that are just coming to the fore: rough set data analysis and inductive logic programming. Its clear and precise presentation is ideal for undergraduate computer science students. The book will also interest those who study artificial intelligence or machine learning at the graduate level. Exercises are provided and each concept is introduced using the same example domain, making it easier to compare the individual properties of different approaches.
More details
Language
English
Place of publication
Cambridge
United Kingdom
Publishing group
Cambridge University Press
Target group
College/higher education
Professional and scholarly
Illustrations
Worked examples or Exercises; 20 Halftones, unspecified; 30 Line drawings, unspecified
File size
21,68 MB
ISBN-13
978-1-139-51187-2 (9781139511872)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
M. E. Mueller
Relational Knowledge Discovery
Book
06/2012
Cambridge University Press
€91.70
Shipment within 15-20 days
M. E. Mueller
Relational Knowledge Discovery
Book
06/2012
Cambridge University Press
€64.20
Shipment within 15-20 days
Person
M. E. Mueller is a Professor of Computer Science at the Bonn-Rhein-Sieg University of Applied Sciences.
Content
1. Introduction; 2. Relational knowledge; 3. From data to hypotheses; 4. Clustering; 5. Information gain; 6. Knowledge and relations; 7. Rough set theory; 8. Inductive logic learning; 9. Ensemble learning; 10. The logic of knowledge; 11. Indexes and bibliography; Bibliography; Index.
