𝔖 Scriptorium
✦   LIBER   ✦
📁

Relational Data Mining

✍ Scribed by Sašo Džeroski (auth.), Sašo Džeroski, Nada Lavrač (eds.)

Publisher
Springer-Verlag Berlin Heidelberg
Year
2001
Tongue
English
Leaves
409
Edition
1
Category
Library
⬇  Acquire This Volume

No coin nor oath required. For personal study only.

✦ Synopsis

As the first book devoted to relational data mining, this coherently written multi-author monograph provides a thorough introduction and systematic overview of the area.
The first part introduces the reader to the basics and principles of classical knowledge discovery in databases and inductive logic programming; subsequent chapters by leading experts assess the techniques in relational data mining in a principled and comprehensive way; finally, three chapters deal with advanced applications in various fields and refer the reader to resources for relational data mining.
This book will become a valuable source of reference for R&D professionals active in relational data mining. Students as well as IT professionals and ambitioned practitioners interested in learning about relational data mining will appreciate the book as a useful text and gentle introduction to this exciting new field.

✦ Table of Contents

Front Matter....Pages I-XIX
Front Matter....Pages 1-1
Data Mining in a Nutshell....Pages 3-27
Knowledge Discovery in Databases: An Overview....Pages 28-47
An Introduction to Inductive Logic Programming....Pages 48-73
Inductive Logic Programming for Knowledge Discovery in Databases....Pages 74-101
Front Matter....Pages 103-103
Three Companions for Data Mining in First Order Logic....Pages 105-139
Inducing Classification and Regression Trees in First Order Logic....Pages 140-159
Relational Rule Induction with CP rogol 4.4: A Tutorial Introduction....Pages 160-188
Discovery of Relational Association Rules....Pages 189-212
Distance Based Approaches to Relational Learning and Clustering....Pages 213-232
Front Matter....Pages 233-233
How to Upgrade Propositional Learners to First Order Logic: A Case Study....Pages 235-261
Propositionalization Approaches to Relational Data Mining....Pages 262-291
Relational Learning and Boosting....Pages 292-306
Learning Probabilistic Relational Models....Pages 307-335
Front Matter....Pages 337-337
Relational Data Mining Applications: An Overview....Pages 339-364
Four Suggestions and a Rule Concerning the Application of ILP....Pages 365-374
Internet Resources on ILP for KDD....Pages 375-388
Back Matter....Pages 389-398

✦ Subjects

Data Mining and Knowledge Discovery; Artificial Intelligence (incl. Robotics); Database Management; Pattern Recognition; Information Storage and Retrieval; Business Information Systems

📜 SIMILAR VOLUMES

Relational data mining
📁 Relational data mining
✍ Sašo Džeroski (auth.), Sašo Džeroski, Nada Lavrač (eds.) 📂 Library 📅 2001 🏛 Springer-Verlag Berlin Heidelberg 🌐 English

<p>As the first book devoted to relational data mining, this coherently written multi-author monograph provides a thorough introduction and systematic overview of the area.<BR>The first part introduces the reader to the basics and principles of classical knowledge discovery in databases and inductiv

Multi-Relational Data Mining
📁 Multi-Relational Data Mining
✍ A.J. Knobbe 📂 Library 📅 2006 🏛 IOS Press 🌐 English

With the increased possibilities in modern society for companies and institutions to gather data cheaply and efficiently, the subject of Data Mining has become of increasing importance. This interest has inspired a rapidly maturing research field with developments both on a theoretical, as well as o

Granular-Relational Data Mining: How to
📁 Granular-Relational Data Mining: How to Mine Relational Data in the Paradigm of Granular Computing?
✍ Piotr Hońko (auth.) 📂 Library 📅 2017 🏛 Springer International Publishing 🌐 English

<p>This book provides two general granular computing approaches to mining relational data, the first of which uses abstract descriptions of relational objects to build their granular representation, while the second extends existing granular data mining solutions to a relational case.<br>Both approa

Parallel data mining for very large rela
📁 Parallel data mining for very large relational databases
✍ Freitas A.A., Lavington S.H. 📂 Library 🌐 English

Data mining, or Knowledge Discovery in Databases (KDD), is of little benefit to commercial enterprises unless it can be carried out efficiently on realistic volumes of data. Operational factors also dictate that KDD should be performed within the context of standard DBMS. Fortunately, relational DBM

Data Mining Using SAS Enterprise Miner
📁 Data Mining Using SAS Enterprise Miner
✍ Randall Matignon 📂 Library 📅 2007 🏛 Wiley-Interscience 🌐 English

<b>The most thorough and up-to-date introduction to data mining techniques using SAS Enterprise Miner. <p>The Sample, Explore, Modify, Model, and Assess (SEMMA) methodology of SAS Enterprise Miner is an extremely valuable analytical tool for making critical business and marketing decisions. Until no