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Business Intelligence and Data Mining

Book Description

“This book is a splendid and valuable addition to this subject. The whole book is well written and I have no hesitation to recommend that this can be adapted as a textbook for graduate courses in Business Intelligence and Data Mining.” Dr. Edi Shivaji, Des Moines, Iowa “As a complete novice to this area just starting out on a MBA course I found the book incredibly useful and very easy to follow and understand. The concepts are clearly explained and make it an easy task to gain an understanding of the subject matter.” -- Mr. Craig Domoney, South Africa. Business Intelligence and Data Mining is a conversational and informative book in the exploding area of Business Analytics. Using this book, one can easily gain the intuition about the area, along with a solid toolset of major data mining techniques and platforms. This book can thus be gainfully used as a textbook for a college course. It is also short and accessible enough for a busy executive to become a quasi-expert in this area in a couple of hours. Every chapter begins with a case-let from the real world, and ends with a case study that runs across the chapters.

Table of Contents

  1. Cover
  2. Half Title Page
  3. Title Page
  4. Copyright
  5. Dedication
  6. Abstract
  7. Contents
  8. Preface
  9. Chapter 1 Wholeness of Business Intelligence and Data Mining
    1. Business Intelligence
    2. Pattern Recognition
    3. Data Processing Chain
    4. Organization of the Book
    5. Review Questions
  10. Section 1
    1. Chapter 2 Business Intelligence Concepts and Applications
      1. BI for Better Decisions
      2. Decision Types
      3. BI Tools
      4. BI Skills
      5. BI Applications
      6. Conclusion
      7. Review Questions
      8. Liberty Stores Case Exercise: Step 1
    2. Chapter 3 Data Warehousing
      1. Design Considerations for DW
      2. DW Development Approaches
      3. DW Architecture
      4. Data Sources
      5. Data Loading Processes
      6. DW Design
      7. DW Access
      8. DW Best Practices
      9. Conclusion
      10. Review Questions
      11. Liberty Stores Case Exercise: Step 2
    3. Chapter 4 Data Mining
      1. Gathering and Selecting Data
      2. Data Cleansing and Preparation
      3. Outputs of Data Mining
      4. Evaluating Data Mining Results
      5. Data Mining Techniques
      6. Tools and Platforms for Data Mining
      7. Data Mining Best Practices
      8. Myths about Data Mining
      9. Data Mining Mistakes
      10. Conclusion
      11. Review Questions
      12. Liberty Stores Case Exercise: Step 3
  11. Section 2
    1. Chapter 5 Decision Trees
      1. Decision Tree Problem
      2. Decision Tree Construction
      3. Lessons from Constructing Trees
      4. Decision Tree Algorithms
      5. Conclusion
      6. Review Questions
      7. Liberty Stores Case Exercise: Step 4
    2. Chapter 6 Regression
      1. Correlations and Relationships
      2. Visual Look at Relationships
      3. Regression Exercise
      4. Nonlinear Regression Exercise
      5. Logistic Regression
      6. Advantages and Disadvantages of Regression Models
      7. Conclusion
      8. Review Exercises
      9. Liberty Stores Case Exercise: Step 5
    3. Chapter 7 Artificial Neural Networks
      1. Business Applications of ANN
      2. Design Principles of an ANN
      3. Representation of a Neural Network
      4. Architecting a Neural Network
      5. Developing an ANN
      6. Advantages and Disadvantages of Using ANNs
      7. Conclusion
      8. Review Exercises
    4. Chapter 8 Cluster Analysis
      1. Applications of Cluster Analysis
      2. Definition of a Cluster
      3. Representing Clusters
      4. Clustering Techniques
      5. Clustering Exercise
      6. K-Means Algorithm for Clustering
      7. Selecting the Number of Clusters
      8. Advantages and Disadvantages of K-Means Algorithm
      9. Conclusion
      10. Review Exercises
      11. Liberty Stores Case Exercise: Step 6
    5. Chapter 9 Association Rule Mining
      1. Business Applications of Association Rules
      2. Representing Association Rules
      3. Algorithms for Association Rule
      4. Apriori Algorithm
      5. Association Rules Exercise
      6. Creating Association Rules
      7. Conclusion
      8. Review Exercises
      9. Liberty Stores Case Exercise: Step 7
  12. Section 3
    1. Chapter 10 Text Mining
      1. Text Mining Applications
      2. Text Mining Process
      3. Mining the TDM
      4. Comparing Text Mining and Data Mining
      5. Text Mining Best Practices
      6. Conclusion
      7. Review Questions
      8. Liberty Stores Case Exercise: Step 8
    2. Chapter 11 Web Mining
      1. Web Content Mining
      2. Web Structure Mining
      3. Web Usage Mining
      4. Web Mining Algorithms
      5. Conclusion
      6. Review Questions
    3. Chapter 12 Big Data
      1. Defining Big Data
      2. Big Data Landscape
      3. Business Implications of Big Data
      4. Technology Implications of Big Data
      5. Big Data Technologies
      6. Management of Big Data
      7. Conclusion
      8. Review Questions
    4. Chapter 13 Data Modeling Primer
      1. Evolution of Data Management Systems
      2. Relational Data Model
      3. Implementing the Relational Data Model
      4. Database Management Systems
      5. Conclusion
      6. Review Questions
  13. Additional Resources
  14. Index