Introduction to Data Mining Using SAS Enterprise Miner 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. I am 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

 

 

"Introduction to Data Mining Using SAS Enterprise Miner is an excellent introduction for students in a classroom setting, or for people learning on their own or in a distance learning mode. The book contains many screen shots of the software during the various scenarios used to exhibit basic data and text mining concepts. In this way, the student obtains validation of correct procedures while performing the steps of the narrative or for parallel processes to complete the assignments.

The author uses a varied and interesting set of databases for demonstration of the many capabilities of SAS Enterprise Miner. The alteration of examples based on structured data and text data sets reinforce the awareness of the need to consider all sources of information and the fact that one can process text with SAS Enterprise Miner in a quite straight-forward manner."

 

James Mentele, Senior Research Fellow
Central Michigan University Research Corporation

 

 

"Introduction to Data Mining Using SAS Enterprise Miner is a useful introduction and guide to the data mining process using SAS Enterprise Miner. Initially the product can be overwhelming, but this book breaks the system into understandable sections. Examples of all the major tools of SAS Enterprise Miner are presented, including useful data analysis techniques of association, clustering, and text mining, as well as data exploration tools. The demonstrations use real-world data with a focus on the input settings needed for analysis and understanding the outputs produced."

 

Jeff G. Zeanah
President, Z Solutions, Inc.

 

 

"Introduction to Data Mining Using SAS Enterprise Miner by Patricia B. Cerrito, a well-known and highly regarded statistician-practitioner and educator, provides a crisp and lively presentation of the subject with neither undue attention nor neglect for technical detail. The book's case study approach is its best strength. Section exercises are presented in timely and contextual fashion. This approach forces the student to think that data has meaning, message, and then the analysis makes more sense."

 

Ravija Badarinathi
Professor, Statistics and Management Science
Cameron School of Business
University of North Carolina Wilmington

 

 

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