Data Preprocessing in Data Mining

  • Salvador García
  • Julián Luengo
  • Francisco Herrera

Part of the Intelligent Systems Reference Library book series (ISRL, volume 72)

Table of contents

  1. Front Matter
    Pages i-xv
  2. Salvador García, Julián Luengo, Francisco Herrera
    Pages 1-17
  3. Salvador García, Julián Luengo, Francisco Herrera
    Pages 19-38
  4. Salvador García, Julián Luengo, Francisco Herrera
    Pages 39-57
  5. Salvador García, Julián Luengo, Francisco Herrera
    Pages 59-105
  6. Salvador García, Julián Luengo, Francisco Herrera
    Pages 107-145
  7. Salvador García, Julián Luengo, Francisco Herrera
    Pages 147-162
  8. Salvador García, Julián Luengo, Francisco Herrera
    Pages 163-193
  9. Salvador García, Julián Luengo, Francisco Herrera
    Pages 195-243
  10. Salvador García, Julián Luengo, Francisco Herrera
    Pages 245-283
  11. Salvador García, Julián Luengo, Francisco Herrera
    Pages 285-313
  12. Back Matter
    Pages 315-320

About this book

Introduction

Data Preprocessing for Data Mining addresses one of the most important issues within the well-known Knowledge Discovery from Data process. Data directly taken from the source will likely have inconsistencies, errors or most importantly, it is not ready to be considered for a data mining process. Furthermore, the increasing amount of data in recent science, industry and business applications, calls to the requirement of more complex tools to analyze it. Thanks to data preprocessing, it is possible to convert the impossible into possible, adapting the data to fulfill the input demands of each data mining algorithm. Data preprocessing includes the data reduction techniques, which aim at reducing the complexity of the data, detecting or removing irrelevant and noisy elements from the data.

This book is intended to review the tasks that fill the gap between the data acquisition from the source and the data mining process. A comprehensive look from a practical point of view, including basic concepts and surveying the techniques proposed in the specialized literature, is given.Each chapter is a stand-alone guide to a particular data preprocessing topic, from basic concepts and detailed descriptions of classical algorithms, to an incursion of an exhaustive catalog of recent developments. The in-depth technical descriptions make this book suitable for technical professionals, researchers, senior undergraduate and graduate students in data science, computer science and engineering.

Keywords

Data Mining Data Preparation Data Preprocessing Data Reduction Discretization Feature Selection Instance Selection Machine Learning Missing Values Noisy Data

Authors and affiliations

  • Salvador García
    • 1
  • Julián Luengo
    • 2
  • Francisco Herrera
    • 3
  1. 1.Department of Computer ScienceUniversity of JaénJaénSpain
  2. 2.Department of Civil EngineeringUniversity of BurgosBurgosSpain
  3. 3.Department of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-10247-4
  • Copyright Information Springer International Publishing Switzerland 2015
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-10246-7
  • Online ISBN 978-3-319-10247-4
  • Series Print ISSN 1868-4394
  • Series Online ISSN 1868-4408
  • About this book