Data Mining: A Hands on Approach for Business Professionals

摘要: Series Foreword. Preface. Acknowledgments. 1. Introduction to Data Mining. What is Mining? Classification Studies (Supervised Learning). Clustering (Unsupervised Visualization. Why Use 1.3 How Do You Mine Data? Preparation. Defining a Study. Reading Your and Building Model. Understanding the Prediction. Mining Models. Decision Trees. Genetic Algorithms. Neural Nets. Agent Network Technology. Hybrid Statistics. Terminology. A Note on Privacy Issues. Summary. 2. The Process. Example. Getting at Data. Qualification Quality Binning. Derivation. Limits. Choosing Good Types of Studies. Elements Analyze? Issues Sampling. 3. Marketplace. (Trends). Vendors. Examples Vendor List. Useful Web Sites/Commercially Available Code. Sites. Finding Sets. Source Sources For 4. Look Angoss: KnowledgeSEEKER. Introduction. More Trees Are Being Used. Looking Different Splits. Going Specific Split. Growing Tree. Forcing Validation. New Scenario for Tree Automatically. Distribution. 5. DataMind. DataMind Read Data/Build Discovery Model Summary Report. Reports. Views. Microsoft Word Evaluation. Perform 6. NeuralWorks Predict. Networks. Corporate America Using Starting Up Training Validating 7. Industry Applications in Banking Finance. Retail. Healthcare. Telecommunications. 8. Enabling Through Warehouses. Warehouse Example An Credit Fraud Retention Management Trends Analysis. Customers are Buying Products. Regional Others. Discussion Adding Our Collection. Creating Set. Study Product/Market Share Market Appendix A. Players. Visualization Tools. Information Access Providers. End User Query EIS Warehousing B. Installing Demo Software. Angoss KnowledgeSEEKER Demo. Windows 3.1. B.1.2 95. Professional Edition |Demo. Predict 3.1, 3.11, or NT 3.5.1. 95 4.0 above. Copying Sample File Local Disk Drive. Help. C. References. Index.