Relational Knowledge Discovery

By: M. E. MÃ¼ller

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Hardcover | 21 June 2012

At a Glance

Hardcover
278 Pages

Dimensions(cm)
24.41 x 16.99 x 1.75

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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.

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You Can Find This Book In

Non-Fiction Science Science in General Computing & I.T.Databases Computer Science Artificial Intelligence Machine Learning

Introduction
Relational knowledge
From data to hypotheses
Clustering
Information gain
Knowledge and relations
Rough set theory
Inductive logic learning
Ensemble learning
The logic of knowledge
Indexes and bibliography
Bibliography
Index
Table of Contents provided by Publisher. All Rights Reserved.

Relational Knowledge Discovery

At a Glance

Hardcover

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How to return your order

Defective items

You Can Find This Book In

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