Introduction to Data Mining and its Applications

verfasst von: Dr. S. Sumathi, Dr. S. N. Sivanandam

Verlag: Springer Berlin Heidelberg

Buchreihe : Studies in Computational Intelligence

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

This book explores the concepts of data mining and data warehousing, a promising and flourishing frontier in data base systems and new data base applications and is also designed to give a broad, yet in-depth overview of the field of data mining. Data mining is a multidisciplinary field, drawing work from areas including database technology, AI, machine learning, NN, statistics, pattern recognition, knowledge based systems, knowledge acquisition, information retrieval, high performance computing and data visualization. This book is intended for a wide audience of readers who are not necessarily experts in data warehousing and data mining, but are interested in receiving a general introduction to these areas and their many practical applications. Since data mining technology has become a hot topic not only among academic students but also for decision makers, it provides valuable hidden business and scientific intelligence from a large amount of historical data. It is also written for technical managers and executives as well as for technologists interested in learning about data mining.

Inhaltsverzeichnis

Backmatter

Titel: Introduction to Data Mining and its Applications
verfasst von: Dr. S. Sumathi
Dr. S. N. Sivanandam
Verlag: Springer Berlin Heidelberg
Electronic ISBN: 978-3-540-34351-6
Print ISBN: 978-3-540-34350-9
DOI: https://doi.org/10.1007/978-3-540-34351-6

Springer Professional

Introduction to Data Mining and its Applications

Über dieses Buch

Inhaltsverzeichnis

Frontmatter

1. Introduction to Data Mining Principles

2. Data Warehousing, Data Mining, and OLAP

3. Data Marts and Data Warehouse

4. Evolution and Scaling of Data Mining Algorithms

5. Emerging Trends and Applications of Data Mining

6. Data Mining Trends and Knowledge Discovery

7. Data Mining Tasks, Techniques, and Applications

8. Data Mining: an Introduction – Case Study

9. Data Mining & KDD

10. Statistical Themes and Lessons for Data Mining

11. Theoretical Frameworks for Data Mining

12. Major and Privacy Issues in Data Mining and Knowledge Discovery

13. Active Data Mining

14. Decomposition in Data Mining - A Case Study

15. Data Mining System Products and Research Prototypes

16. Data Mining in Customer Value and Customer Relationship Management

17. Data Mining in Business

18. Data Mining in Sales Marketing and Finance

19. Banking and Commercial Applications

20. Data Mining for Insurance

21. Data Mining in Biomedicine and Science

22. Text and Web Mining

23. Data Mining in Information Analysis and Delivery

24. Data Mining in Telecommunications and Control

25. Data Mining in Security

Backmatter