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Building Competitive Advantage
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"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!
- Case studies in banking, finance, retail, healthcare, direct marketing, and telecommunications
- The data mining process, start to finish
- Today's newest, most successful approaches and algorithms
- Data mining pitfalls-and how to avoid them
- A close look at industry-leading tools from Angoss and RightPoint
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.
Bloggat om Data Mining
<p> 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
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.