Advanced Techniques in Knowledge Discovery and Data Mining (Advanced Information and Knowledge Processing) [Hardcover]

Nikhil Pal (Editor)

Book Description

Publication Date: June 14, 2005 | Series: Advanced Information and Knowledge Processing

Clear and concise explanations to understand the learning paradigms.

Chapters written by leading world experts.

Editorial Reviews

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 book’s 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 Details

• Hardcover: 272 pages
• Publisher: Springer; 1st Edition. edition (June 14, 2005)
• Language: English
• ISBN-10: 1852338679
• ISBN-13: 978-1852338671
• Product Dimensions: 9.6 x 6.2 x 0.7 inches
• Shipping Weight: 1 pounds (View shipping rates and policies)
• Average Customer Review: 4.0 out of 5 stars  See all reviews (1 customer review)
• Amazon Best Sellers Rank: #4,934,433 in Books (See Top 100 in Books)

Customer Reviews

Most Helpful Customer Reviews

1 of 1 people found the following review helpful:

4.0 out of 5 stars new clustering methods, August 5, 2005

By 

W Boudville (Terra, Sol 3) - See all my reviews
(VINE VOICE)    (TOP 1000 REVIEWER)    (HALL OF FAME REVIEWER)    (REAL NAME)   

This review is from: Advanced Techniques in Knowledge Discovery and Data Mining (Advanced Information and Knowledge Processing) (Hardcover)

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.