Data Preprocessing in Data Mining

1. Front Matter

   Pages i-xv

   PDF

2. Introduction

   • Salvador García, Julián Luengo, Francisco Herrera

   Pages 1-17

3. Data Sets and Proper Statistical Analysis of Data Mining Techniques

   • Salvador García, Julián Luengo, Francisco Herrera

   Pages 19-38

4. Data Preparation Basic Models

   • Salvador García, Julián Luengo, Francisco Herrera

   Pages 39-57

5. Dealing with Missing Values

   • Salvador García, Julián Luengo, Francisco Herrera

   Pages 59-105

6. Dealing with Noisy Data

   • Salvador García, Julián Luengo, Francisco Herrera

   Pages 107-145

7. Data Reduction

   • Salvador García, Julián Luengo, Francisco Herrera

   Pages 147-162

8. Feature Selection

   • Salvador García, Julián Luengo, Francisco Herrera

   Pages 163-193

9. Instance Selection

   • Salvador García, Julián Luengo, Francisco Herrera

   Pages 195-243

10. Discretization

    • Salvador García, Julián Luengo, Francisco Herrera

    Pages 245-283

11. A Data Mining Software Package Including Data Preparation and Reduction: KEEL

    • Salvador García, Julián Luengo, Francisco Herrera

    Pages 285-313

12. Back Matter

    Pages 315-320

    PDF

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.

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

From the book reviews:

“This book is a comprehensive collection of data preprocessing techniques used in data mining. Any readers who practice data mining will find it beneficial … . This book is an excellent guideline in the topic of data preprocessing for data mining. It is suitable for both practitioners and researchers who would like to use datasets in their data mining projects.” (Xiannong Meng, Computing Reviews, December, 2014)

• Department of Computer Science, University of Jaén, Jaén, Spain

  Salvador García

• Department of Civil Engineering, University of Burgos, Burgos, Spain

  Julián Luengo

• Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

  Francisco Herrera

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