Foundations and Advances in Data Mining / Edition 1

by Wesley Chu, Tsau Young Lin

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.

Product Details

ISBN-13:	9783642425387
Publisher:	Springer Berlin Heidelberg
Publication date:	11/15/2014
Series:	Studies in Fuzziness and Soft Computing , #180
Edition description:	2005
Pages:	342
Product dimensions:	6.10(w) x 9.25(h) x 0.03(d)

About the Author

Wesley Chu was born in Taiwan and immigrated to Chicago, Illinois when he was just a pup. It was there he became a Kung Fu master and gymnast. Wesley is an avid gamer and a contributing writer for the magazine Famous Monsters of Filmland. A former stunt man and a member of the SAG, he can also be seen in film and television playing roles such as “Banzai Chef” in Fred Claus and putting out Oscar worthy performances as a bank teller in Chicago Blackhawks commercials.

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.