Practical Data Mining

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Overview

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 ...

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More About This Book

Overview

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.

http://www.celestech.com/PracticalDataMining

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Editorial Reviews

From the Publisher
Achieves a unique and delicate balance between depth, breadth, and clarity.
—Stefan Joe-Yen, Cognitive Research Engineer, Northrop Grumman Corporation & Adjunct Professor, Department of Computer Science, Webster University

Used as a primer for the recent graduate or as a refresher for the grizzled veteran, Practical Data Mining is a must-have book for anyone in the field of data mining and analytics.
—Chad Sessions, Program Manager, Advanced Analytics Group (AAG)

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Product Details

  • ISBN-13: 9781439868362
  • Publisher: Taylor & Francis
  • Publication date: 1/9/2012
  • Pages: 304
  • Product dimensions: 6.00 (w) x 9.40 (h) x 0.80 (d)

Meet the Author

Monte F. Hancock, Jr., BA, MS, is Chief Scientist for Celestech, Inc., which has offices in Falls Church, Virginia, and Phoenix, Arizona. He was also a Technical Fellow at Northrop Grumman; Chief Cognitive Research Scientist for CSI, Inc., and was a software architect and engineer at Harris corporation, and HRB Singer, Inc. He has over 30 years of industry experience in software engineering and data mining technology development.

He is also Adjunct Full Professor of Computer Science for the Webster University Space Coast Region, where he serves as Program Mentor for the Master of Science Degree in Computer Science. Monte has served for 26 years on the adjunct faculty in the Mathematics and Computer Science Department of the Hamilton Holt School of Rollins College, Winter Park, Florida, and served 3 semesters as adjunct Instructor in Computer Science at Pennsylvania State University.

Monte teaches secondary Mathematics, AP Physics, Chemistry, Logic, Western Philosophy, and Church History at New Covenant School, and New Testament Greek at Heritage Christian Academy, both in Melbourne, Florida. He was a mathematics curriculum developer for the Department of Continuing Education of the University of Florida in Gainesville, and serves on the Industry Advisory Panels in Computer Science for both the Florida Institute of Technology, and Brevard Community College in Melbourne, Florida. Monte has twice served on panels for the National Science Foundation.

Monte has served on many program committees for international data mining conferences, was a Session Chair for KDD. He has presented 15 conference papers, edited several book chapters, and co-authored the book Data Mining Explained with Rhonda Delmater, Digital Press, 2001.

Monte is cited in (among others):

  • "Who’s Who in the World" (2009–2012)
  • "Who’s Who in America" (2009–2012)
  • "Who’s Who in Science and Engineering" (2006–2012)
  • "Who’s Who in the Media and Communication" (1st ed.)
  • "Who’s Who in the South and Southwest" (23rd–25th ed.)
  • "Who’s Who Among America’s Teachers" (2006, 2007)
  • "Who’s Who in Science and Theology" (2nd ed.)
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Table of Contents

What Is Data Mining and What Can It Do?
Introduction
A Brief Philosophical Discussion
The Most Important Attribute of the Successful Data Miner: Integrity
What Does Data Mining Do?
What Do We Mean By Data?
Data Complexity
Computational Complexity
Summary

The Data Mining Process
 Introduction
Discovery and Exploitation
Eleven Key Principles of Information Driven Data Mining
Key Principles Expanded
Type of Models: Descriptive, Predictive, Forensic
Data Mining Methodologies
A Generic Data Mining Process
RAD Skill Set Designators
Summary

Problem Definition (Step 1)
 Introduction
Problem Definition Task 1: Characterize Your Problem
Problem Definition Checklist
Candidate Solution Checklist
Problem Definition Task 2: Characterizing Your Solution
Problem Definition Case Study
Summary

Data Evaluation (Step 2)
 Introduction
Data Accessibility Checklist
How Much Data Do You Need?
Data Staging
Methods Used for Data Evaluation
Data Evaluation Case Study: Estimating the Information Content Features
Some Simple Data Evaluation Methods
Data Quality Checklist
Summary

Feature Extraction and Enhancement (Step 3)
 Introduction: A Quick Tutorial on Feature Space
Characterizing and Resolving Data Problems
Principal Component Analysis
Synthesis of Features
Degapping
Summary

Prototyping Plan and Model Development (Step 4)
 Introduction
Step 4A: Prototyping Plan
Prototyping Plan Case Study
Step 4B: Prototyping/Model Development
Model Development Case Study
Summary

Model Evaluation (Step 5)
 Introduction
Evaluation Goals and Methods
What Does Accuracy Mean?
Summary

Implementation (Step 6)
 Introduction
Quantifying the Benefits of Data Mining
Tutorial on Ensemble Methods
Getting It Wrong: Mistakes Every Data Miner Has Made
Summary

Supervised Learning Genre Section 1—Detecting and Characterizing Known Patterns
 Introduction
Representative Example of Supervised Learning: Building a Classifier
Specific Challenges, Problems, and Pitfalls of Supervised Learning
Recommended Data Mining Architectures for Supervised Learning
Descriptive Analysis
Predictive Modeling
Summary

Forensic Analysis Genre Section 2—Detecting, Characterizing, and Exploiting Hidden Patterns
 Introduction
Genre Overview
Recommended Data Mining Architectures for Unsupervised Learning
Examples and Case Studies for Unsupervised Learning
Tutorial on Neural Networks
Making Syntactic Methods Smarter: The Search Engine Problem
Summary

Genre Section 3—Knowledge: Its Acquisition, Representation, and Use
 Introduction to Knowledge Engineering
Computing with Knowledge
Inferring Knowledge from Data: Machine Learning
Summary

References
Glossary
Index

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