Foundations and Advances in Data Mining
Author:   Wesley Chu ,  Tsau Young Lin
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Edition:   2005 ed.
Volume:   180
ISBN:  

9783540250579


Pages:   342
Publication Date:   15 September 2005
Format:   Hardback
Availability:   Out of stock   Availability explained
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Foundations and Advances in Data Mining


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Overview

With the growing use of information technology and the recent advances in web systems, the amount of data available to users has increased exponentially. Thus, there is a critical need to understand the content of the data. As a result, data-mining has become a popular research topic in recent years for the treatment of the ""data rich and information poor"" syndrome. In this carefully edited volume a theoretical foundation as well as important new directions for data-mining research are presented. It brings together a set of well respected data mining theoreticians and researchers with practical data mining experiences. The presented theories will give data mining practitioners a scientific perspective in data mining and thus provide more insight into their problems, and the provided new data mining topics can be expected to stimulate further research in these important directions.

Full Product Details

Author:   Wesley Chu ,  Tsau Young Lin
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Edition:   2005 ed.
Volume:   180
Dimensions:   Width: 15.50cm , Height: 2.00cm , Length: 23.40cm
Weight:   0.694kg
ISBN:  

9783540250579


ISBN 10:   3540250573
Pages:   342
Publication Date:   15 September 2005
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Out of stock   Availability explained
The supplier is temporarily out of stock of this item. It will be ordered for you on backorder and shipped when it becomes available.

Table of Contents

The Mathematics of Learning.- Logical Regression Analysis: From Mathematical Formulas to Linguistic Rules.- A Feature/Attribute Theory for Association Mining and Constructing the Complete Feature Set.- A New Theoretical Framework for K-means-type Clustering.- Clustering via Decision Tree Construction.- Incremental Mining on Association Rules.- Mining Association Rules from Tabular Data Guided by Maximal Frequent Itemsets.- Sequential Pattern Mining by Pattern-Growth: Principles and Extensions.- Web Page Classification.- Web Mining – Concepts, Applications, and Research Directions.- Privacy-Preserving Data Mining.

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