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• Part I: A Map of the Territory: The World, Knowledge and Models. Translating Experience. Modeling and Mining: Putting It Together. Part II: Business Modeling: What is a Model? Framing Business Models. Getting the Right Model. Getting the Model Right. Deploying the Model. Part III: Data Mining: Getting Started. What Mining Tools Do. Getting the Initial Model: Basic Practices of Data Mining. Improving the Mined Model. Deploying the Mined Model. Part IV: Methodology: Methodology. Resources.
• (source: Nielsen Book Data)

Business Modeling and Data Mining demonstrates how real world business problems can be formulated so that data mining can answer them. The concepts and techniques presented in this book are the essential building blocks in understanding what models are and how they can be used practically to reveal hidden assumptions and needs, determine problems, discover data, determine costs, and explore the whole domain of the problem. This book articulately explains how to understand both the strategic and tactical aspects of any business problem, identify where the key leverage points are and determine where quantitative techniques of analysis -- such as data mining -- can yield most benefit. It addresses techniques for discovering how to turn colloquial expression and vague descriptions of a business problem first into qualitative models and then into well-defined quantitative models (using data mining) that can then be used to find a solution. The book completes the process by illustrating how these findings from data mining can be turned into strategic or tactical implementations. Teaches how to discover, construct and refine models that are useful in business situations Teaches how to design, discover and develop the data necessary for mining Provides a practical approach to mining data for all business situations Provides a comprehensive, easy-to-use, fully interactive methodology for building models and mining data Provides pointers to supplemental online resources, including a downloadable version of the methodology and software tools.
(source: Nielsen Book Data)

• The world, knowledge, and models
• Translating experience
• Modeling and mining : putting it together
• Framing business models
• Getting the right model
• Getting the model right
• Deploying the model
• Getting started
• What mining tools do
• Getting the initial model : basic practices of data mining
• Improving the model
• Deploying the mined model
• Methodology.

Business Modeling and Data Mining demonstrates how real world business problems can be formulated so that data mining can answer them. The concepts and techniques presented in this book are the essential building blocks in understanding what models are and how they can be used practically to reveal hidden assumptions and needs, determine problems, discover data, determine costs, and explore the whole domain of the problem. This book articulately explains how to understand both the strategic and tactical aspects of any business problem, identify where the key leverage points are and determine where quantitative techniques of analysis -- such as data mining -- can yield most benefit. It addresses techniques for discovering how to turn colloquial expression and vague descriptions of a business problem first into qualitative models and then into well-defined quantitative models (using data mining) that can then be used to find a solution. The book completes the process by illustrating how these findings from data mining can be turned into strategic or tactical implementations. Teaches how to discover, construct and refine models that are useful in business situations Teaches how to design, discover and develop the data necessary for mining Provides a practical approach to mining data for all business situations Provides a comprehensive, easy-to-use, fully interactive methodology for building models and mining data Provides pointers to supplemental online resources, including a downloadable version of the methodology and software tools.

• Preface Introduction
• Chapter 1 Data Exploration As a Process
• Chapter 2 The Nature of the World and Its Impact on Data Preparation
• Chapter 3 Data Preparation as a Process
• Chapter 4 Getting the Data: Basic Preparation
• Chapter 5 Sampling, Variability and Confidence
• Chapter 6 Handling Non-Numerical Variables
• Chapter 7 Normalizing and Redistributing Variables
• Chapter 8 Replacing Missing and Empty Values
• Chapter 9 Series Variables
• Chapter 10 Preparing the Data Set
• Chapter 11 The Data Survey
• Chapter 12 Using Prepared Data Appendix A Using the Demonstration Code on the CD Appendix B Further Reading Index.
• (source: Nielsen Book Data)

"Data Preparation for Data Mining" addresses an issue unfortunately ignored by most authorities on data mining: data preparation. Thanks largely to its perceived difficulty, data preparation has traditionally taken a backseat to the more alluring question of how best to extract meaningful knowledge. But without adequate preparation of your data, the return on the resources invested in mining is certain to be disappointing. Dorian Pyle corrects this imbalance. A twenty-five-year veteran of what has become the data mining industry, Pyle shares his own successful data preparation methodology, offering both a conceptual overview for managers and complete technical details for IT professionals. Apply his techniques and watch your mining efforts pay off-in the form of improved performance, reduced distortion, and more valuable results. On the enclosed CD-ROM, you'll find a suite of programs as C source code and compiled into a command-line-driven toolkit. This code illustrates how the author's techniques can be applied to arrive at an automated preparation solution that works for you. Also included are demonstration versions of three commercial products that help with data preparation, along with sample data with which you can practice and experiment. It: offers in-depth coverage of an essential but largely ignored subject; goes far beyond theory, leading you-step by step-through the author's own data preparation techniques; provides practical illustrations of the author's methodology using realistic sample data sets; includes algorithms you can apply directly to your own project, along with instructions for understanding when automation is possible and when greater intervention is required; explains how to identify and correct data problems that may be present in your application; and, prepares miners, helping them head into preparation with a better understanding of data sets and their limitations.
(source: Nielsen Book Data)