Introduction to Data Mining and its Applications

✍ Scribed by Dr. S. Sumathi, Dr. S. N. Sivanandam (auth.)

• Publisher: Springer-Verlag Berlin Heidelberg
• Year: 2006
• Tongue: English
• Leaves: 835
• Series: Studies in Computational Intelligence 29
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-xxii
Introduction to Data Mining Principles....Pages 1-20
Data Warehousing, Data Mining, and OLAP....Pages 21-73
Data Marts and Data Warehouse....Pages 75-150
Evolution and Scaling of Data Mining Algorithms....Pages 151-164
Emerging Trends and Applications of Data Mining....Pages 165-183
Data Mining Trends and Knowledge Discovery....Pages 185-194
Data Mining Tasks, Techniques, and Applications....Pages 195-216
Data Mining: an Introduction – Case Study....Pages 217-229
Data Mining & KDD....Pages 231-241
Statistical Themes and Lessons for Data Mining....Pages 243-263
Theoretical Frameworks for Data Mining....Pages 265-270
Major and Privacy Issues in Data Mining and Knowledge Discovery....Pages 271-291
Active Data Mining....Pages 293-302
Decomposition in Data Mining - A Case Study....Pages 303-313
Data Mining System Products and Research Prototypes....Pages 315-320
Data Mining in Customer Value and Customer Relationship Management....Pages 321-386
Data Mining in Business....Pages 387-409
Data Mining in Sales Marketing and Finance....Pages 411-438
Banking and Commercial Applications....Pages 439-472
Data Mining for Insurance....Pages 473-498
Data Mining in Biomedicine and Science....Pages 499-543
Text and Web Mining....Pages 545-589
Data Mining in Information Analysis and Delivery....Pages 591-613
Data Mining in Telecommunications and Control....Pages 615-627
Data Mining in Security....Pages 629-648
Back Matter....Pages 649-828

✦ Subjects

Appl.Mathematics/Computational Methods of Engineering; Artificial Intelligence (incl. Robotics)