RT Web Page DB /z-wcorg/ DS http://worldcat.org ID 796214849 LA English UL http://dx.doi.org/10.1017/CBO9781139047869 T1 Relational knowledge discovery A1 Müller, M. E., PB Cambridge University Press PP New York YR 2012 SN 9781139518185 1139518186 9781139047869 1139047868 1280773812 9781280773815 9781139516334 1139516337 AB 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.