Discovering Knowledge in Data: An Introduction to Data Mining

by Daniel T. Larose

The field of data mining lies at the confluence of predictive analytics, statistical analysis, and business intelligence. Due to the ever-increasing complexity and size of data sets and the wide range of applications in computer science, business, and health care, the process of discovering knowledge in data is more relevant than ever before.This book provides the tools needed to thrive in today's big data world. The author demonstrates how to leverage a company's existing databases to increase profits and market share, and carefully explains the most current data science methods and techniques. The reader will "learn data mining by doing data mining". By adding chapters on data modelling preparation, imputation of missing data, and multivariate statistical analysis, Discovering Knowledge in Data, Second Edition remains the eminent reference on data mining. The second edition of a highly praised, successful reference on data mining, with thorough coverage of big data applications, predictive analytics, and statistical analysis. Includes new chapters on Multivariate Statistics, Preparing to Model the Data, and Imputation of Missing Data, and an Appendix on Data Summarization and Visualization Offers extensive coverage of the R statistical programming language Contains 280 end-of-chapter exercises Includes a companion website for university instructors who adopt the book

Genres: Software, Academic

336 pages, Kindle Edition

First published November 18, 2004

Reviews

[Gabri · Rating 3 out of 5]

Mandatory read for school, did not finish around 20%.

The chapters are easy to follow. The chapter on Data preprocessing was very valuable to me, explaining why it's so important and which (statistical) methods could be used.
Unfortunately the way the results were being obtained (old version of SPSS) were not so relevant for me since in this course I have to use Python. It does seem like a good book to learn how Data Mining algorithms work and how to interpret its results.

[Stancalău · Rating 4 out of 5]

Really got me interested in the domain of data mining. It is rather an old book and I would have liked some more hands-on examples in a more modern mining tool. Nevertheless, worth the read.