Used by corporations, industry, and government to inform and fuel everything from focused advertising to homeland security, data mining can be a very useful tool across a wide range of applications. Unfortunately, most books on the subject are designed for the computer scientist and statistical illuminati and leave the reader largely adrift in technical waters.
Revealing the lessons known to the seasoned expert, yet rarely written down for the uninitiated, Practical Data Mining explains the ins-and-outs of the detection, characterization, and exploitation of actionable patterns in data. This working field manual outlines the what, when, why, and how of data mining and offers an easy-to-follow, six-step spiral process. Catering to IT consultants, professional data analysts, and sophisticated data owners, this systematic, yet informal treatment will help readers answer questions, such as:
- What process model should I use to plan and execute a data mining project?
- How is a quantitative business case developed and assessed?
- What are the skills needed for different data mining projects?
- How do I track and evaluate data mining projects?
- How do I choose the best data mining techniques?
Helping you avoid common mistakes, the book describes specific genres of data mining practice. Most chapters contain one or more case studies with detailed projects descriptions, methods used, challenges encountered, and results obtained. The book includes working checklists for each phase of the data mining process. Your passport to successful technical and planning discussions with management, senior scientists, and customers, these checklists lay out the right questions to ask and the right points to make from an insider’s point of view.
Visit the book’s webpage for access to additional resources—including checklists, figures, PowerPoint slides, and a small set of simple prototype data mining tools.
Table of contents
- Title Page
- About the Author
- Chapter 1: What Is Data Mining and What Can It Do?
- Chapter 2: The Data Mining Process
- Chapter 3: Problem Definition (Step 1)
- Chapter 4: Data Evaluation (Step 2)
- Chapter 5: Feature Extraction and Enhancement (Step 3)
- Chapter 6: Prototyping Plan and Model Development (Step 4)
- Chapter 7: Model Evaluation (Step 5)
- Chapter 8: Implementation (Step 6)
- Chapter 9: Supervised Learning Genre Section 1—Detecting and Characterizing Known Patterns
- Chapter 10: Forensic Analysis Genre Section 2—Detecting, Characterizing, and Exploiting Hidden Patterns
- Chapter 11: Genre Section 3—Knowledge: Its Acquisition, Representation, and Use
- Title: Practical Data Mining
- Release date: February 2012
- Publisher(s): CRC Press
- ISBN: 9781466551381
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