Advanced Techniques in Knowledge Discovery and Data Mining...

Features

• Cover Type: Hard Cover with 272 pages
• Published by: Springer
• Edition: 1st Edition June 14, 2005
• Written in: English
• ISBN 10 Number: 1852338679
• ISBN 13 Number: 978-1852338671
• Book Dimensions: 9.5 x 6.1 x 0.7 inches
• Weighs: 1 pounds

Product Review


From the reviews:

"This book presents research on some of the recent advances in the field of DMKD, and provides a glimpse into some real-world applications.  book starts with a preface by the editors, including background information as well as an overview of the books contents.  The overall layout and the length of the volume  appear to be satisfactory.  postgraduate students and the faculty members in the business intelligence or DMKD fields would find this volume to be a useful addition to their libraries." (C. S. Arora, Computing Reviews, April, 2006)

Product Description


This book presents research on some of the most recent advances in data mining and knowledge discovery, providing theory as well as its applications on practical real world applications. The methodologies discussed encompass tools like Bayesian networks, and major facets of computational intelligence paradigms.

Contributions from top class researchers include:

• Recent trends in data mining and knowledge discovery
• Advanced data mining techniques in semi-conductor manufacturing
• Clustering and visualization in retail markets baskets
• Segmentation of continuous data
• Instance selection using evolutionary algorithms
• Cooperative co-evolution for data mining of Bayesian networks
• Knowledge discovery and data mining in medicine
• Satellite image classification
• Knowledge discovery using rough sets

This book presents both practical detail and some of the most up-to-date theory in the field, useful for postgraduates and those who wish to develop applications using advanced data mining and knowledge discovery techniques.

Reader Reviews
Data mining methods continue to improve, and this book gives you a good sense of where the field is currently at. A wide gamut of ideas. For example, there is a good discussion of new clustering techniques. Closely related, and in practice inseparable, are visualisation methods that can be applied to such clusters. The sheer mass of information in the clusters makes strong visualisation a necessity for a manual comprehension of the data. If nothing else, it can be used to see if the clusters make sense, in the context of your application. The text describes an example implementation, to retail data. But a careful reading of the methods show that they are potentially quite general.