Foundations of Rule Learning / by Johannes Fürnkranz, Dragan Gamberger, Nada Lavrač.

Format:

Book

Author/Creator:

Fürnkranz, Johannes, author.

Gamberger, Dragan, author.

Lavrač, Nada, author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Cognitive technologies 1611-2482

Cognitive Technologies, 1611-2482

Language:

English

Subjects (All):

Data mining.

Artificial intelligence.

Pattern perception.

Computers.

Statistics.

Data Mining and Knowledge Discovery.

Artificial Intelligence.

Pattern Recognition.

Computation by Abstract Devices.

Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.

Local Subjects:

Data Mining and Knowledge Discovery.

Artificial Intelligence.

Pattern Recognition.

Computation by Abstract Devices.

Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.

Physical Description:

1 online resource (XVIII, 334 pages).

Edition:

First edition 2012.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2012.

System Details:

text file PDF

Summary:

Rules - the clearest, most explored and best understood form of knowledge representation - are particularly important for data mining, as they offer the best tradeoff between human and machine understandability. This book presents the fundamentals of rule learning as investigated in classical machine learning and modern data mining. It introduces a feature-based view, as a unifying framework for propositional and relational rule learning, thus bridging the gap between attribute-value learning and inductive logic programming, and providing complete coverage of most important elements of rule learning. The book can be used as a textbook for teaching machine learning, as well as a comprehensive reference to research in the field of inductive rule learning. As such, it targets students, researchers and developers of rule learning algorithms, presenting the fundamental rule learning concepts in sufficient breadth and depth to enable the reader to understand, develop and apply rule learning techniques to real-world data.

Contents:

Part I. Introduction to Rule Learning

Machine Learning and Data Mining

Propositional Rule Learning

Relational Rule Learning

Part II. Elements of Rule Learning

Formal Framework for Rule Analysis

Features

Heuristics

Pruning of Rules and Rule Sets

Survey of Classification Rule Learning Systems Through the Analysis of Rule Learning Elements Used

Part III. Selected Topics in Predictive Induction

Part IV Selected Techniques and Applications.

Other Format:

Printed edition:

ISBN:

978-3-540-75197-7

9783540751977

Access Restriction:

Restricted for use by site license.