Computing Reviews
Today's Issue Hot Topics Search Browse Recommended My Account Log In
Review Help
Search
Data preprocessing in data mining
García S., Luengo J., Herrera F., Springer Publishing Company, Incorporated, Cham, Switzerland, 2015. 320 pp. Type: Book (978-3-319102-46-7)
Date Reviewed: Dec 16 2014

This book is a comprehensive collection of data preprocessing techniques used in data mining. Any readers who practice data mining will find it beneficial, as it provides detailed descriptions of various data preprocessing techniques ranging from dealing with missing values and noisy data, to data reduction and discretization, to feature selection and instance selection.

Data mining plays an increasingly important role in today’s research, business, and society. Because the amount of data available is skyrocketing and we are all eager to figure out the meaning of this data, data mining becomes a critical tool to manage and use available data. However, data, especially that collected from real applications, is often incomplete, inaccurate in presentation, and often not suitable for direct use by a data mining process. This book surveys the technologies in data preprocessing methods that prepare the raw data for use by various data mining processes.

The book contains ten chapters. Chapter 1 introduces the concept of data preprocessing and its relation with data mining. Chapter 2 describes dataset properties and appropriate statistical tests for these properties. Chapter 3 establishes the basic models of data preparation, including integration, cleaning, and transformation. Chapters 4 through 9 are devoted to various data preprocessing techniques. They include dealing with missing values, dealing with noisy data, data reduction, feature selection, instance selection, and discretization. The last chapter is an overview of a data mining software package, knowledge extraction based on evolutionary learning (KEEL), that is widely used in data mining with rich data preprocessing features.

Each chapter in the book, especially the ones discussing specific areas of data preprocessing, is an independent module. Each one starts with an abstract and an introduction of the concepts, followed by a detailed description of the data preprocessing technology and the needed math tools. Each chapter ends with a comprehensive list of references, ranging anywhere from about 30 to over 170, which gives readers an excellent starting point if they would like to pursue the topics further.

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.

Reviewer:  Xiannong Meng Review #: CR143016 (1503-0212)
Bookmark and Share
  Reviewer Selected
Featured Reviewer
 
 
Data Mining (H.2.8 ... )
 
 
Content Analysis And Indexing (H.3.1 )
 
Would you recommend this review?
yes
no
Other reviews under "Data Mining": Date
Feature selection and effective classifiers
Deogun J. (ed), Choubey S., Raghavan V. (ed), Sever H. (ed) Journal of the American Society for Information Science 49(5): 423-434, 1998. Type: Article
May 1 1999
Rule induction with extension matrices
Wu X. (ed) Journal of the American Society for Information Science 49(5): 435-454, 1998. Type: Article
Jul 1 1998
Predictive data mining
Weiss S., Indurkhya N., Morgan Kaufmann Publishers Inc., San Francisco, CA, 1998. Type: Book (9781558604032)
Feb 1 1999
more...

E-Mail This Printer-Friendly
Send Your Comments
Contact Us
Reproduction in whole or in part without permission is prohibited.   Copyright 1999-2024 ThinkLoud®
Terms of Use
| Privacy Policy