Data Mining Methods and Applications
eBook - PDF

Data Mining Methods and Applications

  1. 336 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Data Mining Methods and Applications

Book details
Table of contents
Citations

About This Book

With today's information explosion, many organizations are now able to access a wealth of valuable data. Unfortunately, most of these organizations find they are ill-equipped to organize this information, let alone put it to work for them. Gain a Competitive Advantage Employ data mining in research and forecasting Build models with data management

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Yes, you can access Data Mining Methods and Applications by Kenneth D. Lawrence, Stephan Kudyba, Ronald K. Klimberg in PDF and/or ePUB format, as well as other popular books in Business & Information Management. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Auerbach Publications
Year
2007
ISBN
9781420013733
Edition
1
Topic
Business
Subtopic
Information Management
Index
Business

Table of contents

  1. Front cover
  2. Dedications
  3. Contents
  4. Preface
  5. About the Editors
  6. Editors and Contributors
  7. PART I: TECHNIQUES OF DATA MINING
  8. Chapter 1. An Approach to Analyzing and Modeling Systems for Real-Time Decisions
  9. Chapter 2. Ensemble Strategies for Neural Network Classifiers
  10. Chapter 3. Neural Network Classification with Uneven Misclassification Costs and Imbalanced Group Sizes
  11. Chapter 4. Data Cleansing with Independent Component Analysis
  12. Chapter 5. A Multiple Criteria Approach to Creating Good Teams over Time
  13. PART II: APPLICATIONS OF DATA MINING
  14. Chapter 6. Data Mining Applications in Higher Education
  15. Chapter 7. Data Mining for Market Segmentation with Market Share Data: A Case Study Approach
  16. Chapter 8. An Enhancement of the Pocket Algorithm with Ratchet for Use in Data Mining Applications
  17. Chapter 9. Identifcation and Prediction of Chronic Conditions for Health Plan Members Using Data Mining Techniques
  18. Chapter 10. Monitoring and Managing Data and Process Quality Using Data Mining: Business Process Management for the Purchasing and Accounts Payable Processes
  19. Chapter 11. Data Mining for Individual Consumer Models and Personalized Retail Promotions
  20. PART III: OTHER AREAS OF DATA MINING
  21. Chapter 12. Data Mining: Common Definitions, Applications, and Misunderstandings
  22. Chapter 13. Fuzzy Sets in Data Mining and Ordinal Classification
  23. Chapter 14. Developing an Associative Keywork Space of the Data Mining Literature through Latent Semantic Analysis
  24. Chapter 15. A Classification Model for a Two-Class (New Product Purchase) Disrcimination Process Using Multiple-Criteria Linear Programming
  25. Index
  26. Back cover