ROBERT GROTH has worked in the high tech arena for over 14 years and has consulted for many Fortune 500 companies on large-scale data mining projects. He is also the author of the successful Hands-On SQL
"Finally, here's a book that explains in plain English what data mining is and how it's used to improve a company's bottom line . . . Groth takes a very complex and vast field and makes it comprehensible." Miguel A. Castro, Ph.D., President, Dovetail Solutions
Data mining business solutions-practical, up-to-date, and hands-on!
With data mining, you can achieve competitive advantage from the data you've already paid to compile. Data Mining: Building Competitive Advantage shows you how. You won't just learn the theory and concepts of data mining; you'll discover how to apply them-hands-on, through real applications!
Whether you're a manager, marketer, consultant, analyst, or database professional, Robert Groth will help you master data mining-and deliver all the competitive advantage it promises.
About the Website
The accompanying website includes full trial editions of two of the world's leading desktop data mining tools, Angoss KnowledgeSEEKER® and RightPoint DataCruncher.
Click here for a sample chapter for this book: 0130862711.pdf
I. STARTING OUT.1. Introduction to Data Mining.
What Is Data Mining? Why Use Data Mining? Case Studies of Implementing Data Mining. A Process for Successfully Deploying Data Mining for Competitive Advantage. A Note on Privacy Issues. Summary.2. Getting Started with Data Mining.
Classification (Supervised Learning). Clustering (Unsupervised Learning). A Clustering Example. Visualization. Association (Market Basket). Assortment Optimization. Prediction. Estimation. Summary.3. The Data-Mining Process.
Discussion of Data-Mining Methodology. The Example. Data Preparation. Defining a Study. Reading the Data and Building a Model. Understanding Your Model. Prediction. Summary.4. Data-Mining Algorithms.
Introduction. Decision Trees. Genetic Algorithms. Neural Networks. Bayesian Belief Networks. Statistics. Advanced Algorithms for Association. Algorithms for Assortment Optimization. Summary.5. The Data-Mining Marketplace.
Introduction (Trends). Data-Mining Vendors. Visualization. Useful Web Sites/Commercially Available Code. Data Sources For Mining. Summary.
II. A RAPID TUTORIAL.6. A Look at Angoss: KnowledgeSEEKER.
Introduction. Data Preparation. Defining the Study. Building the Model. Understanding the Model. Prediction. Summary.7. A Look at RightPoint DataCruncher.
Introduction. Data Preparation. Defining the Study. Read Your Data/Build a Discovery Model. Understanding the Model. Perform Prediction. Summary.
III. INDUSTRY FOCUS.8. Industry Applications of Data Mining.
Data-Mining Applications in Banking and Finance. Data-Mining Applications in Retail. Data-Mining Applications in Healthcare. Data-Mining Applications in Telecommunications. Summary.9. Enabling Data Mining through Data Warehouses.
Introduction. A Data-Warehouse Example in Banking and Finance. A Data-Warehouse Example in Retail. A Data-Warehouse Example in Healthcare. A Data-Warehouse Example in Telecommunications. Summary.Appendix A: Data-Mining Vendors.
Data-Mining Players. Visualization Tools. Useful Web Sites. Information Access Providers. Data-Warehousing Vendors.Appendix B: Installing Demo Software.
Installing Angoss KnowledgeSEEKER Demo. Installing the RightPointPoint DataCruncher Demo.Appendix C: References.
In the two short years since the first version of this book was published, the data-mining industry has progressed at nothing short of light speed. Just look at a few of the more significant events:
Version One of this book emphasized all the attention data mining has recently received, citing many sources such as an article in Bank Systems & Technology, January 1996, which stated: "Data mining is the most important application in financial services in 1996." In a 1996 commercial by IBM, played during the SuperBowl, fashion models discuss the use and advantages of data mining. Finally, there was a graph from the META Group projecting the data-mining market to be a $800 million dollar market by the year 2000.
Data mining is still gaining momentum and the players are rapidly changing. A second version of this book was needed to update discussions on current players and industry trends. For example, there is a major push in today's industry to change from a tools-oriented focus to a more solution-oriented focus.
This version of the book greatly expands on how data mining solves business problems. You the reader want to understand not only the current trends in the industry, but also what data mining is and how it can be applied to provide competitive advantage. META Group made the comment: "The majority of global 2000 organizations will find data-mining technologies to be critical to their business success by the year 2000." While this is interesting, there are specific reasons why this statement is true. The burning questions you should be asking are: Why are global 2000 organizations finding data mining to be "critical"? What are the benefits of data mining, both to me and my business? How do I make the most of data mining?
This text, Data Mining: Building Competitive Advantage, resulted from the revelation that data mining is becoming mainstream and that there are few books about data mining devoted to the business professional. It provides an innovative, easy approach to learning data mining for business professionals, students, and consultants. The CD-ROM at the back of the book makes learning data mining a hands-on activity. You can try out different software packages available for data mining and learn how these tools are being used to solve industry problems.
This book focuses on how knowledge discovery is used in different industries, and discusses several of the data-mining software products available. Sample studies are provided for specific industries, including retail, banking, insurance, and healthcare.
This text takes a different approach to introducing data mining than the academic books currently on the market. The focus of this book is on industry applications, discussions of specific business problems, and a hands-on teaching style to demonstrate how tools can be used to attain business benefit.
This book provides answers to the following basic questions:
Data mining is an evolving field, with great variety in terminology and methodology. To gain a reasonable understanding of data mining, you should have a broad perspective on how it is being used within the industry today. Data-mining tools currently on the market are also discussed, as well as how to get more information on the vendors and Web sites available to you.
This book covers industry applications of data mining in various industries, including:
This book broadens the scope of what is relevant to learning data mining. Not only should you learn the methodology and terminology needed to use data mining, you should also learn specific examples of how to achieve fast results in the corporate environment.
You never hear as much as you should about industry solutions of data mining. Most companies are reluctant to discuss findings that lead to dramatic returns on investment, for competitive reasons. Industry applications making use of data-mining technology drive competitive advantage. People use data-mining technology to predict outcomes: which customers are likely to respond to specific marketing campaigns, claims that are fraudulent, or products customers are most likely to buy. The more success a company has in predicting such outcomes, the more tight-lipped they are prone to become.
This book also provides a hands-on approach to learning data mining. By devoting three hours of your time, you can use the enclosed CD-ROM to familiarize yourself with data mining's major processes.
Once we cover the concepts of data mining, we'll go directly to exercises that show the ease of turning data into information. The CD-ROM contains demonstrations of two data-mining tools: Angoss® KnowledgeSeekerTM, and RightPoint® Software's DataCruncher.
This book gives a general overview of data mining and is written for a broad-based audience. The book will be useful to:
Anyone in business who deals with large amounts of data should consider the data-mining tools and applications described in this book. Effort is made to provide industry examples as well as to make the use of data-mining products understandable.
Database administrators should be interested in this book, since it explains how end users can extract data from relational databases and data warehouses in order to mine data. Sample data structures are described for different industries. The data fields used in different types of data-mining studies are also discussed in detail.
Data mining is especially useful to marketing organizations, because it allows them to profile customers to a previously unavailable level. Some people refer to this as "one-to-one" marketing. In general, today's distributors of mass mailings use data-mining tools. In several years, data mining will be a mandatory strategic requirement of marketing organizations.
Students who desire a practical introduction to the basics of data mining and the current market can start with this book.
Consultants can benefit from the discussions of the vendors involved and by industry-specific examples.
Data Mining: Building Competitive Advantage does not include detailed explanations of the algorithms used with data mining. If you want to learn more about the algorithms, I would suggest Advances in Knowledge Discovery and Data Mining, by Usama M. Fayyad, Gregory Piatestsky-Shapiro, Padhraic Smyth, and Ramasamy Uthurusam. This book, at over 550 pages, is the most comprehensive work available today on the technical approaches used in data mining.
This book is devoted to the business professional and targets an audience of professionals who do not necessarily have a statistics background and who want to learn about data-mining applications, or who wish to attempt data mining.
Data Mining: Building Competitive Advantage is divided into three parts:
The first chapters introduce data mining, discuss the data-mining process, and cover vendors involved in this market.
Chapter 1, "Introduction to Data Mining," introduces basic concepts of data mining and explains why data mining is important.
Chapter 2, "Getting Started With Data Mining," discusses several of the approaches taken in data mining and their potential benefits.
Chapter 3, "The Data-Mining Process," covers the process of data mining and introduces different types of studies as well as data-preparation issues.
Chapter 4, "Data-Mining Algorithms," discusses the types of algorithms and technologies being used today.
Chapter 5, "The Data-Mining Marketplace," introduces vendors in the data-mining market today, and includes discussion of applications such as SAS Enterprise Miner and IBM's Intelligent Miner.
Chapters 6 and 7 introduce two leading data-mining software products.
Chapter 6, "A Look at Angoss: KnowledgeSEEKER," covers the leading, commercial data-mining software product, which is based on a decision-tree model and is focused on end users. A business example for the healthcare industry is discussed.
Chapter 7, "A Look at RightPoint DataCruncher," covers an innovative commercial data-mining software product that is focused on marketing professionals. A business example for the telecommunications industry is discussed.
Chapters 8 and 9 focus on specific industry uses of data mining. Examples for each study performed are provided, with tips on how these can be performed on corporate database systems.
Chapter 8, "Industry Applications of Data Mining," looks at types of data-mining studies in banking and finance, retail, healthcare, and the telecommunications industry. Examples of companies performing data mining are provided.
Chapter 9, "Enabling Data Mining Through Data Warehouses," looks at how data warehouses provide a methodology for helping perform data-mining studies. Four data-warehouse industry examples are provided to discuss the type of data that would be integrated and introduce how some data-mining studies could be performed using these data warehouses.
The minimum system requirements for installing the CD-ROM included in this book are discussed in Appendix B. Each of the data-mining software products included in the CD-ROM have their own requirements.
The installed software enables you to run the CD-ROM-based tutorial included in this book. Additional files have been added specifically for this book beyond those provided by Angoss Software and RightPoint Software.