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Real-World Data Mining: Applied Business Analytics and Decision Making

Book Description

Use the latest data mining best practices to enable timely, actionable, evidence-based decision making throughout your organization! Real-World Data Mining demystifies current best practices, showing how to use data mining to uncover hidden patterns and correlations, and leverage these to improve all aspects of business performance.

Drawing on extensive experience as a researcher, practitioner, and instructor, Dr. Dursun Delen delivers an optimal balance of concepts, techniques and applications. Without compromising either simplicity or clarity, he provides enough technical depth to help readers truly understand how data mining technologies work. Coverage includes: processes, methods, techniques, tools, and metrics; the role and management of data; text and web mining; sentiment analysis; and Big Data integration. Throughout, Delen's conceptual coverage is complemented with application case studies (examples of both successes and failures), as well as simple, hands-on tutorials.

Real-World Data Mining will be valuable to professionals on analytics teams; professionals seeking certification in the field; and undergraduate or graduate students in any analytics program: concentrations, certificate-based, or degree-based.

Table of Contents

  1. About This eBook
  2. Title Page
  3. Copyright Page
  4. Books in the FT Press Analytics Series
  5. Dedication Page
  6. Contents
  7. Foreword
  8. Acknowledgments
  9. About the Author
  10. 1. Introduction to Analytics
    1. Is There a Difference Between Analytics and Analysis?
    2. Where Does Data Mining Fit In?
    3. Why the Sudden Popularity of Analytics?
    4. The Application Areas of Analytics
    5. The Main Challenges of Analytics
    6. A Longitudinal View of Analytics
    7. A Simple Taxonomy for Analytics
    8. The Cutting Edge of Analytics: IBM Watson
    9. References
  11. 2. Introduction to Data Mining
    1. What Is Data Mining?
    2. What Data Mining Is Not
    3. The Most Common Data Mining Applications
    4. What Kinds of Patterns Can Data Mining Discover?
    5. Popular Data Mining Tools
    6. The Dark Side of Data Mining: Privacy Concerns
    7. References
  12. 3. The Data Mining Process
    1. The Knowledge Discovery in Databases (KDD) Process
    2. Cross-Industry Standard Process for Data Mining (CRISP-DM)
    3. SEMMA
    4. SEMMA Versus CRISP-DM
    5. Six Sigma for Data Mining
    6. Which Methodology Is Best?
    7. References
  13. 4. Data and Methods in Data Mining
    1. The Nature of Data in Data Mining
    2. Preprocessing of Data for Data Mining
    3. Data Mining Methods
    4. Prediction
    5. Classification
    6. Decision Trees
    7. Cluster Analysis for Data Mining
    8. k-Means Clustering Algorithm
    9. Association
    10. Apriori Algorithm
    11. Data Mining Misconceptions and Realities
    12. References
  14. 5. Data Mining Algorithms
    1. Nearest Neighbor
    2. Similarity Measure: The Distance Metric
    3. Artificial Neural Networks
    4. Support Vector Machines
    5. Linear Regression
    6. Logistic Regression
    7. Time-Series Forecasting
    8. References
  15. 6. Text Analytics and Sentiment Analysis
    1. Natural Language Processing
    2. Text Mining Applications
    3. The Text Mining Process
    4. Text Mining Tools
    5. Sentiment Analysis
    6. References
  16. 7. Big Data Analytics
    1. Where Does Big Data Come From?
    2. The Vs That Define Big Data
    3. Fundamental Concepts of Big Data
    4. The Business Problems That Big Data Analytics Addresses
    5. Big Data Technologies
    6. Data Scientists
    7. Big Data and Stream Analytics
    8. Data Stream Mining
    9. References
  17. Index