Data and Text Mining: A Business Applications Approach [Paperback]

Thomas W. Miller (Author)

Book Description

Publication Date: April 16, 2004 | ISBN-10: 0131400851 | ISBN-13: 978-0131400856 | Edition: 1

This conceptual introduction to data mining within the context of business and marketing research provides an eclectic approach to the field. Using worked examples and business case studies, the volume answers the four questions: why is data mining important to business and marketing research; how is data mining different from other types of research; what do we learn from data mining; and how do we do data mining? The book explains data mining, traditional methods, data-adaptive methods and applications in business and marketing with business cases and provides lists of tables, figures and exhibits. For Managers and Analysts.

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To learn how a company can grow their business by harnessing more information, you need Data and Text Mining.

Inside this book you will find a manager's introduction to Data and Text Mining. The emphasis is on business data, including information about firms and markets, products and prices, supplier actions and buyer responses. By focusing on business applications such as customer relationship management, database marketing, market segmentation, sales forecasting, and more, you'll be shown just, how Data and Text Mining results can be used to guide business decision making.

Written in a non-technical, non-mathematical style, this book is an excellent tool for any course where learning to make decisions based on real data is important!

Excerpt. © Reprinted by permission. All rights reserved.

Firms collect consumer responses from telephone, mail, and online surveys. They scan data from retail sales. They record business transactions and log text from focus groups, online bulletin boards, and user groups. Spurred on by lower costs of data acquisition, storage, retrieval, and analysis, business databases grow larger each day. Business managers work in a world in which data are plentiful and well-formulated theories rare. This is a world well suited to data and text mining.

Data and text mining represent flexible approaches to information management, research, and analysis. They are data-driven rather than theorydriven. They rely upon powerful computers and efficient algorithms. Relatively new and little understood by business and marketing managers, data and text mining are important enough to require an adequate introduction. That is the reason for this book.

This book advocates a disciplined approach to data and text analysis. It is through the development of meaningful models that data and text mining contribute to information management, research, and analysis. Models should fit the data, yielding small errors of prediction and classification. Models should be as simple as possible because simple, parsimonious models are easy to understand and use. Model selection in data and text mining is a matter of striking the proper balance between fit and parsimony. When analysts strike the proper balance, they develop models with explanatory power.

To serve as a business introduction to data and text mining, a book cannot rely upon statistics and computer algorithms alone. A business book must give students a feeling for the work of data and text mining and how it serves business needs. This book focuses upon business applications, including customer relationship management, database marketing, consumer choice modeling, market segmentation, market response modeling, sales forecasting, and the analysis of corporate databases. It reviews traditional and data-adaptive methods and shows how the results of data and text mining can be used to guide business decision making.

The book provides an introduction to data and text mining methods and applications. It shows how to use tools for data manipulation and integration, statistical graphics, traditional statistics, and data-adaptive methods. It shows output from data and text mining programs and reviews the literature, citing relevant books and articles in business, marketing research, statistics, computer science, and information management.

The book draws upon a rich set of business cases and data sets described at length in Appendix A. Cases promote experiential learning; students learn about data and text mining by doing data and text mining. Case documentation and data sets have been placed in the public domain, available on the Web site for the book. Additional cases and discussion are provided in Miller (2004).

Data and text mining offer great promise as technologies for learning about customers, competitors, and markets. But having the ability to organize and analyze large quantities of data does not excuse us from our obligation to conduct research in a responsible manner. Appendix B reviews the important topic of privacy in business research.

Recognizing that business and research professionals have strong feelings about computing software and systems, our coverage of data and text mining topics is sufficiently broad to accommodate users of many systems. The Web site for the book provides data, documentation, and examples for use with various software systems.

Examples in the book were prepared using S-PLUS, Insightful Miner, R, and Perl. Many leading researchers in statistics use S-PLUS and R, providing a substantial body of public-domain code for data mining applications. The Perl user community provides an extensive set of utilities for text processing. By relying upon public-domain systems and code, we can do more work for less cost, and we can write programs that run on many computer platforms. Both R and Perl, for example, have Apple Macintosh OS X, Microsoft Windows, Linux, and Unix implementations.

The book can serve as a textbook in business, marketing research, statistics, management information systems, computer science, information science, quantitative methods, decision science, and operations research. It may be used as a standalone introduction to data and text mining or as a technical reference for practitioners. Written in a non-technical, nonmathematical style, the book is accessible to many readers.

I have many people to thank for making this book possible. Wendy Craven of Prentice Hall was a key proponent of the book throughout its development, always willing to listen to ideas for making the book relevant to a wide range of business disciplines. Rebecca Cummings and John Roberts of Prentice Hall assisted in the final stages of production. Special recognition is due to Dana H. James for copyediting and indexing and to Amy Hendrickson, 'Ij3Xnology, Inc., for her assistance in the development of IfEX class and style files. Data entry, proofreading, graphics, and electronic typesetting services were provided by Teresa Cheng, Kristin Gill, and Krista Sorenson. Kim Kok, Giovanni Marchisio, Jeff Scott, and Michael Sannella of Insightful Corporation provided advice and technical assistance in the area of text mining. Hung T. Nguyen helped in writing the supplement for instructors. Reviewers and colleagues provided many helpful suggestions. For their feedback and encouragement in the reviewing process, I thank Lynd Bacon, Jerry L. Oglesby of SAS Institute Inc., David M. Smith of Insightful Corporation, and Michel Wedel. Most of all, my wife Chris and son Daniel stood by me in good times and bad, tolerating my unusual writer's lifestyle.

Thomas W. Miller
Madison, Wisconsin

Product Details

• Paperback: 192 pages
• Publisher: Prentice Hall; 1 edition (April 16, 2004)
• Language: English
• ISBN-10: 0131400851
• ISBN-13: 978-0131400856
• Product Dimensions: 9 x 6.8 x 0.4 inches
• Shipping Weight: 10.4 ounces (View shipping rates and policies)
• Average Customer Review: 2.5 out of 5 stars  See all reviews (2 customer reviews)
• Amazon Best Sellers Rank: #1,534,492 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

4.0 out of 5 stars Good Introduction to Data and Text Mining, December 21, 2008

By 

John M. Ford "johnDC" (near DC, MD USA) - See all my reviews
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This review is from: Data and Text Mining: A Business Applications Approach (Paperback)

I appreciate a book that lands in my "zone of proximal development"--that efficiently reviews familiar material to establish context, then extends my knowledge into new areas. Thomas Miller's five-chapter introduction to data mining is appropriate continuing education for researchers who want to learn data-adaptive methods and text analysis. It was "in the zone" for me when it first came out. I keep it around as a model of how to explain data and text mining to others.

In Chapter 1 ("What is Data Mining?"), Miller compares data mining and traditional research, discussing not only specific statistical procedures, but different approaches to selecting a statistical model and the requirements for testing an emergent model with new data. Miller warns researchers about claims made by software vendors--this type of analysis is not automatic. We should not underestimate resources needed to identify appropriate data sources, restructure data, and clean up missing and miscoded data.

Chapter 2 ("Traditional Methods") reviews multiple regression, logistic regression, principle components analysis and cluster analysis. Miller illustrates principles of data preparation and reduction. He emphasizes the need to partition data into training sets to develop models, validation sets to compare models, and test sets to evaluate a selected model. He stresses the role of parsimony and goodness of fit in selecting the best model.

Chapter 3 overviews "Data Adaptive Methods" appropriate for large data sets with many variables. These techniques produce models which emerge from the data. Challenges include choosing between many possible models and prioritizing relationships between variables when large numbers of observations make most relationships statistically significant. Miller reviews data visualization techniques, decision tree procedures, smoothing methods that make patterns more interpretable, and association-driven neural networks which "learn" to find patterns.

The fourth chapter ("Text Mining") presents procedures and resources needed to prepare text for quantitative analysis. It introduces issues ranging from quick-and-dirty text data "munging" (reformatting) using Perl scripts to the core concepts of natural language processing. Miller shows how to transform text data into a "term by document" matrix that can be analyzed with statistical procedures. He explains how (and why) to capture information about sentence syntax, root words, word frequencies, phrases, and other text features.

This chapter explores the potential of creating "text measures" by "scoring documents based on predefined measurement categories" (p. 120). The most basic text measure uses the frequency of specific words to produce a score on a predefined dimension, such as Realism or Optimism. Content "dictionaries" or lists of carefully selected words can be constructed to measure text on many such dimensions. Miller sees promise for text measures in marketing research. There is also potential for those interested in resumes (see Kathryn Troutman's Federal Resume Guidebook) and other employment documents.

The fifth chapter ("And in Conclusion...") describes project management strategies that help data mining projects succeed. Two appendices contain information about the example data sets and caution researchers about data mining privacy concerns. The author provides most statistics and text manipulation algorithms in public domain tools such R and Perl, although he refers to the commercial tools S-PLUS and Insightful Miner as well.

Additional topics could have been included in a longer version of the book. Some statistical procedures could have been treated in greater depth and research design issues from content analysis (see Krippendorff's Content Analysis: An Introduction to Its Methodology) could have been included in the discussion of text mining. But overall the author has made reasonable compromises for an introductory text. It's a good and useful read.

3 of 5 people found the following review helpful:

1.0 out of 5 stars Data & Text Mining by T. W. Miller- Ask for a refund, September 11, 2006

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Ramnath A. Lakshmi-ratan - See all my reviews
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This review is from: Data and Text Mining: A Business Applications Approach (Paperback)

One of the main reasons I bought the book was the promise of case data and sample code especially in R (and splus). However, the prentice Hall site had only presentation slides (pdf files) and no data or code. Moreover their own tech support had no cluse as to why these files were missing.
This is a classic example of overpromise and underdeliver. I would avoid this book in the future.