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Introduction to Data Mining with case studies

By: Material type: TextTextPublication details: New Delhi Prentice-Hall of India 2006Description: xvii, 457 pages : illustrations ; 24 cmISBN:
  • 9788120330535
Subject(s): DDC classification:
  • 005.74 GUP-I
Contents:
Preface. 1. Introduction. CASE STUDY 1A: Data Mining Techniques for Optimizing Inventories for Electronic Commerce. CASE STUDY 1B: Crime Data Mining: A General Framework and Some Examples. 2. Association Rules Mining. CASE STUDY 2: Mining Customer Value: From Association Rules to Direct Marketing. 3. Classification. CASE STUDY 3A: KDD Insurance Risk Assessment: A Case Study. CASE STUDY 3B: A Data Mining Approach for Retailing Bank Customer Attrition Analysis. 4. Cluster Analysis. CASE STUDY 4: Efficient Clustering of Very Large Document Collections. 5. Web Data Mining. CASE STUDY 5: Lessons and Challenges from Mining Retail E-Commerce Data. 6. Search Engines. CASE STUDY 6: The Anatomy of a Large-Scale Hypertextual Web Search Engine. 7. Data Warehousing. CASE STUDY 7: Data Warehouse Governance: Best Practices at Blue Cross and Blue Shield of North Carolina. 8. Online Analytical Processing (OLAP). CASE STUDY 8: Discovering Web Access Patterns and Trends by Applying OLAP and Data Mining Technology on Web Logs. 9. Information Privacy and Data Mining. CASE STUDY 9: Privacy Conflicts in CRM Services for Online Shops: A Case Study. Index.
Summary: The field of data mining provides techniques for automated discovery of most valuable information from the accumulated data of computerized operations of enterprises. This book offers a clear and comprehensive introduction to both data mining theory and practice. It is written primarily as a textbook for students of computer science, management, computer applications, and information technology. The book ensures that students are exposed to all major data mining techniques without the mathematical rigour that one would prefer to use. To enhance the understanding of the concepts introduced, and to show how the techniques described in the book are used in practice, each chapter is followed by one or two case studies that have been published in scholarly journals. Studying case studies will provide the reader with a lot of insight into data mining. The book also provides many examples, end-of-chapter exercises, and a good list of references and Web resources especially those which are easy to understand and useful for students.
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Book Book Dept. of Computational Biology and Bioinformatics Processing Center Dept. of Computational Biology and Bioinformatics 005.74 GUP-I (Browse shelf(Opens below)) Available DCB966

"Easter economy edition"--Cover.

Preface. 1. Introduction. CASE STUDY 1A: Data Mining Techniques for Optimizing Inventories for Electronic Commerce. CASE STUDY 1B: Crime Data Mining: A General Framework and Some Examples. 2. Association Rules Mining. CASE STUDY 2: Mining Customer Value: From Association Rules to Direct Marketing. 3. Classification. CASE STUDY 3A: KDD Insurance Risk Assessment: A Case Study. CASE STUDY 3B: A Data Mining Approach for Retailing Bank Customer Attrition Analysis. 4. Cluster Analysis. CASE STUDY 4: Efficient Clustering of Very Large Document Collections. 5. Web Data Mining. CASE STUDY 5: Lessons and Challenges from Mining Retail E-Commerce Data. 6. Search Engines. CASE STUDY 6: The Anatomy of a Large-Scale Hypertextual Web Search Engine. 7. Data Warehousing. CASE STUDY 7: Data Warehouse Governance: Best Practices at Blue Cross and Blue Shield of North Carolina. 8. Online Analytical Processing (OLAP). CASE STUDY 8: Discovering Web Access Patterns and Trends by Applying OLAP and Data Mining Technology on Web Logs. 9. Information Privacy and Data Mining. CASE STUDY 9: Privacy Conflicts in CRM Services for Online Shops: A Case Study. Index.

The field of data mining provides techniques for automated discovery of most valuable information from the accumulated data of computerized operations of enterprises. This book offers a clear and comprehensive introduction to both data mining theory and practice. It is written primarily as a textbook for students of computer science, management, computer applications, and information technology. The book ensures that students are exposed to all major data mining techniques without the mathematical rigour that one would prefer to use. To enhance the understanding of the concepts introduced, and to show how the techniques described in the book are used in practice, each chapter is followed by one or two case studies that have been published in scholarly journals. Studying case studies will provide the reader with a lot of insight into data mining. The book also provides many examples, end-of-chapter exercises, and a good list of references and Web resources especially those which are easy to understand and useful for students.

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