Book Details

Instructors may access teaching resources by clicking the ‘Request Instructor Resources’ tab next to the title.
Please note that you can subscribe to a maximum of 2 titles.

Data Mining: Introductory and Advanced Topics, 1/e


Data Mining: Introductory and Advanced Topics, 1/e
Author(s)  Margaret H. Dunham
ISBN  9788177587852
Imprint  Pearson Education
Copyright  2006
Pages  328
Binding  Paperback
List Price  Rs. 935.00
  
 
 

Market: For undergraduate courses in Computer Science & Information Technology / MCA In this book the author provides the reader with a comprehensive coverage of data mining topics and algorithms. Data base perspective is maintained throughout the book which provides students with a focused discussion of algorithms, data structures, data types and complexity of algorithms and space. It also emphasizes the use of data mining concepts in real-world applications with large database components.

  • About the Authors
  • Contents
  • Features
  • Downloadable Resources

Margaret H. Dunham received the B.A. and the M.S. in mathematics from Miami University in Oxford, Ohio. She earned the Ph.D. degree in computer science from Southern Methodist University. Professor Dunham’s research interests encompass main memory databases, data mining, temporal databases, and mobile computing. She is currently an Associate Editor for IEEE Transactions on Knowledge and Data Engineering. She has published numerous technical papers in such research areas as database concurrency control and recovery, database machines, main memory databases, and mobile computing.
S. Sridhar is currently the director of Arunai Engineering College, Tiruvannamalai, Tamil Nadu, India.
 

  1. Introduction
    1. Introduction
    2. Related Concepts
    3. Data Mining Techniques
  2. Core Topics
    1. Classification
    2. Clustering
    3. Association Rules
  3. Advanced Topics
    1. Web Mining
    2. Spatial Mining
    3. Temporal Mining
    • Appendix
    • Index
 

  • Covers advanced topics such as Web Mining and Spatial/Temporal Mining.

  • Includes succinct coverage of Data Warehousing, OLAP, Multidimensional Data, and Preprocessing.

  • Concise coverage on distributed, parallel, and incremental algorithms.

  • Provides case studies.

  • Offers clearly written algorithms to better understand techniques.

  • Algorithms are presented in a pseudocode.

  • Includes a reference on how to use Prototypes and DM products.
 
Instructor Resources
 


 
Username/ Email  
Password  
If you are new to this site, and you do not have a username and password, please register.