Machine Learning and Data Mining

Author: Kononenko   Igor;Kukar   Matjaz  

Publisher: Elsevier Science‎

Publication year: 2007

E-ISBN: 9780857099440

P-ISBN(Paperback): 9781904275213

P-ISBN(Hardback):  9781904275213

Subject: TP Automation Technology , Computer Technology;TP39 computer application

Language: ENG

Access to resources Favorite

Disclaimer: Any content in publications that violate the sovereignty, the constitution or regulations of the PRC is not accepted or approved by CNPIEC.

Description

Data mining is often referred to by real-time users and software solutions providers as knowledge discovery in databases (KDD). Good data mining practice for business intelligence (the art of turning raw software into meaningful information) is demonstrated by the many new techniques and developments in the conversion of fresh scientific discovery into widely accessible software solutions. This book has been written as an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining.

Suitable for advanced undergraduates and their tutors at postgraduate level in a wide area of computer science and technology topics as well as researchers looking to adapt various algorithms for particular data mining tasks. A valuable addition to the libraries and bookshelves of the many companies who are using the principles of data mining (or KDD) to effectively deliver solid business and industry solutions.

  • Provides an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining
  • A valuable addition to the libraries and bookshelves of companies using the principles of data mining (or KDD) to effectively deliver solid business and industry solutions

Chapter

Cover

pp.:  1 – 3

ABOUT THE AUTHORS

pp.:  3 – 4

Copyright

pp.:  5 – 6

Table of Contents

pp.:  6 – 16

Foreword

pp.:  16 – 18

Preface

pp.:  18 – 22

Chapter 1 Introduction

pp.:  22 – 58

Chapter 2 Learning and Intelligence

pp.:  58 – 80

Chapter 3 Machine Learning Basics

pp.:  80 – 128

Chapter 4 Knowledge Representation

pp.:  128 – 152

Chapter 5 Learning as Search

pp.:  152 – 174

Chapter 6 Measures for Evaluating the Quality of Attributes

pp.:  174 – 202

Chapter 7 Data Preprocessing

pp.:  202 – 234

Chapter 8 *Constructive Induction

pp.:  234 – 248

Chapter 9 Symbolic Learning

pp.:  248 – 280

Chapter 10 Statistical Learning

pp.:  280 – 296

Chapter 11 Artificial Neural Networks

pp.:  296 – 342

Chapter 12 Cluster Analysis

pp.:  342 – 380

Chapter 13 **Learning Theory

pp.:  380 – 414

Chapter 14 **Computational Learning Theory

pp.:  414 – 444

Appendix A *Definitions of some lesser known terms

pp.:  444 – 450

References

pp.:  450 – 468

Index

pp.:  468 – 476

The users who browse this book also browse


No browse record.