Skip to main content
Free carbon-neutral shipping over $39.99  ·  Thousands of books on sale — sale ends soon

Soft Computing for Data Mining Applications (Hardback)

by K. R. Venugopal

Springer

Hardback or Cased Book 341 pages Mar 11, 2009

(No reviews yet) Write a Review
Millions of titlesEvery book you're looking for
Carbon-neutral shippingFree on orders over $39.99
Fast shipping worldwideDelivered to your door
The authors have consolidated their research work in this volume titled Soft Computing for Data Mining Applications. The monograph gives an insight into the research in the ?elds of Data Mining in combination with Soft Computing methodologies. In these days, the data continues to grow - ponentially. Much of the data is implicitly or explicitly imprecise. Database discovery seeks to discover noteworthy, unrecognized associations between the data items in the existing database. The potential of discovery comes from the realization that alternate contexts may reveal additional valuable information. The rate at which the data is storedis growing at a phenomenal rate. Asaresult, traditionaladhocmixturesofstatisticaltechniquesanddata managementtools are no longer adequate for analyzing this vast collection of data. Severaldomainswherelargevolumesofdataarestoredincentralizedor distributeddatabasesincludesapplicationslikeinelectroniccommerce, bio- formatics, computer security, Web intelligence, intelligent learning database systems, ?nance, marketing, healthcare, telecommunications, andother?elds. E?cient tools and algorithms for knowledge discovery in large data sets have been devised during the recent years. These methods exploit the ca- bility of computers to search huge amounts of data in a fast and e?ective manner. However, the data to be analyzed is imprecise and a?icted with - certainty. In the case of heterogeneous data sources such as text and video, the data might moreover be ambiguous and partly con?icting. Besides, p- terns and relationships of interest are usually approximate. Thus, in order to make the information mining process more robust it requires tolerance toward imprecision, uncertainty and exceptions.
Author:
K. R. Venugopal
Publisher:
Springer
Publication Date:
Mar 11, 2009
Number of pages:
341 pages
Binding:
Hardback or Cased Book
ISBN-10:
3642001920
ISBN-13:
9783642001925