Overview

If you have an abundance of data, but no idea what to do with it, this book was written for you! Packed with examples from an array of industries, Introduction to Data Mining Using SAS Enterprise Miner provides you with excellent starting points and practical guidelines to begin data mining today. Author Patricia Cerrito encourages you to think of data mining as a process of exploration rather than as a collection of tools to investigate data. In that way, you choose the methods that will extract the most information from your data, and, while there are no right answers to investigating data sets, there are many questions that can be asked to produce meaningful results. Each answer then creates a path that helps you drill down to explore the data fully. It is up to you to determine what is of interest and what is important to analyze.

In addition to providing a basic introduction to the processes and techniques of data mining using SAS Enterprise Miner software, the book also addresses:

Visualizing and summarizing data

Using clustering, association rules, and predictive modeling

Using SAS Text Miner software for text analysis

Writing accurate and effective reports about your data mining results

This book is intended for a broad audience interested in learning about the practical aspects of data mining. Readers should have some basic knowledge of SAS and statistics.

Full Product Details

9781590478295

Table of Contents

Reviews

Dr. Patricia Cerrito, professor of mathematics at the University of Louisville, has written a very useful introduction to SAS Enterprise Miner and data mining. Dr. Cerrito is a well-known expert in data mining, and is especially well known for her research in applications of text mining. Her experience in the classroom is clearly evident in the examples and motivation of her material. She begins the text with an easy-to-follow introduction to SAS Enterprise Miner. She follows the introductory chapter with an important overview and discussion of data visualization techniques, providing clear illustrations conducted in SAS Enterprise Miner. A unique feature of this book is the early introduction of text mining, which Dr. Cerrito does in Chapter 3. The following chapters examine data summarization, association (or market basket analysis), the use of text mining in combination with association analysis, and cluster analysis. The emphasis on these exploratory or pattern recognition techniques in data mining is a helpful addition to the literature illustrating the application of SAS Enterprise Miner. Dr. Cerrito's expertise in these areas is evident. The book concludes with an excellent chapter on predictive modeling techniques and on time series techniques. The final chapter is particularly unique: Dr. Cerrito provides the student with an excellent review of technical preparation. Many students often fail to understand the importance of communicating the results of their analyses either to other scientists or senior managers. This chapter provides the student with clear examples of good report preparation. This is a wonderful introductory text to data mining and to SAS Enterprise Miner. Iam confident that faculty, students, and business analysts will find this book to be an invaluable resource. -- J. Michael Hardin, Ph.D., Associate Dean for Research Culverhouse College of Commerce, The University of Alabama

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