Fw: [computational.science] New Book - Data Mining and Knowledge Discovery Technologies

> Data Mining and Knowledge Discovery Technologies
>
> ISBN: 978-1-59904-960-1; 379 pp; December 2008
>
> Published under the imprint IGI Publishing (formerly Idea Group
Publishing)
>
> http://www.igi-global.com/books/details.asp?id=7139
<http://www.igi-global.com/books/details.asp?id=7139>
>
>
>
> Edited by: David Taniar, Monash University, Australia
>
>
>
> DESCRIPTION
>
> As information technology continues to advance in massive increments, the
bank of information available from personal, financial, and business
electronic transactions and all other electronic documentation and data
storage is growing at an exponential rate. With this wealth of information
comes the opportunity and necessity to utilize this information to maintain
competitive advantage and process information effectively in real-world
situations.
>
>
>
> Data Mining and Knowledge Discovery Technologies presents researchers and
practitioners in fields such as knowledge management, information science,
Web engineering, and medical informatics, with comprehensive, innovative
research on data mining methods, structures, tools, and methods, the
knowledge discovery process, and data marts, among many other cutting-edge
topics.
>
>
>
> ****************************************
>
> "This volume covers important foundations to researches and applications
in data mining, covering association rules, clustering, and classification,
as well as new directions in domain driven and model free data mining."
>
> - David Taniar, Monash University, Australia
>
> ****************************************
>
>
>
> TABLE OF CONTENTS
>
> Section I: Association Rules
>
>
>
> Chapter I: OLEMAR: An Online Environment for Mining Association Rules in
Multidimensional Data
>
> Riadh Ben Messaoud, Laboratory ERIC -University of Lyon 2, France
>
> Sabine Loudcher Rabasda, Laboratory ERIC -University of Lyon 2, France
>
> Rokia Missaoui, Laboratory LARIM - University of Québec, Canada
>
> Omar Boussaid, Laboratory ERIC University of Lyon 2, France
>
>
>
> Chapter II: Current Interestingness Measures for Association Rules: What
do they Really Measure?
>
> Yun Sing Koh, Auckland University of Technology, New Zealand
>
> Richard O'Keefe, University of Otago, New Zealand
>
> Nathan Rountree, University of Otago, New Zealand
>
>
>
> Chapter III: Mining Association Rules from XML Data
>
> Qin Ding, East Carolina University, USA
>
> Gnanasekaran Sundarraj, Pennsylvania State University at Harrisburg, USA
>
>
>
> Chapter IV: A Lattice-Based Framework for Interactively and Incrementally
Mining Web Traversal Patterns
>
> Yue-Shi Lee, Ming Chuan University, Taiwan
>
> Show-Jane Yen, Ming Chuan University, Taiwan
>
>
>
> Section II: Clustering and Classification
>
>
>
> Chapter V: Determination of Optimal Clusters Using a Genetic Algorithm
>
> Tushar, Indian Institute of Technology, India
>
> Shibendu Shekhar Roy, Indian Institute of Technology, India
>
> Dilip Kumar Pratihar, Indian Institute of Technology, India
>
>
>
> Chapter VI: K-Means Clustering Adopting rbf-Kernel
>
> ABM Shawkat Ali, Central Queensland University, Australia
>
>
>
> Chapter VII: Advances in Classification of Sequence Data
>
> Pradeep Kumar, Institute for Development and Research in Banking
Technology, India
>
> P.Radha Krishna, Institute for Development and Research in Banking
Technology, India
>
> Raju. S. Bapi, University of Hyderabad, India
>
> T. M. Padmaja, Institute for Development and Research in Banking
Technology, India
>
>
>
> Chapter VIII: Using Cryptography For Privacy-Preserving Data Mining
>
> Justin Zhan, Carnegie Mellon University
>
>
>
> Section III: Domain Driven and Model Free
>
>
>
> Chapter IX: Domain Driven Data Mining
>
> Longbing Cao, University of Technology, Sydney, Australia
>
> Chengqi Zhang, University of Technology, Sydney, Australia
>
>
>
> Chapter X: Model Free Data Mining
>
> Can Yang, Zhejiang University, China
>
> Jun Meng, Xi'an Jiao Tong University, China
>
> Shanan Zhu, Zhejiang University, China
>
> Mingwei Dai, Xi'an Jiao Tong University, China
>
>
>
> Section IV: Issues and Applications
>
>
>
> Chapter XI: Minimizing the Minus Sides of Mining Data
>
> John Wang, Montclair State University, USA
>
> Xiaohua Hu, Drexel University, USA
>
> Dan Zhu, Iowa State University,USA
>
>
>
> Chapter XII: Study of Protein-Protein Interactions from Multiple Data
Sources
>
> Tu Bao Ho, Japan Advanced Institute of Science and Technology, Japan
>
> Thanh Phuong Nguyen, Japan Advanced Institute of Science and Technology,
Japan
>
> Tuan Nam Tran, Japan Advanced Institute of Science and Technology, Japan
>
>
>
> Chapter XIII: Data Mining in the Social Sciences and Iterative Attribute
Elimination
>
> Anthony Scime, SUNY Brockport, USA
>
> Gregg R. Murray, SUNY Brockport, USA
>
> Wan Huang, SUNY Brockport, USA
>
> Carol Brownstein-Evans, Nazareth College, USA
>
>
>
> Chapter XIV: A Machine Learning Approach for One-Stop Learning
>
> Marco A. Alvarez, Utah State University, USA
>
> SeungJin Lim, Utah State University, USA
>
>
>
> For more information about Data Mining and Knowledge Discovery
Technologies, you can view the title information sheet at
http://www.igi-global.com/downloads/pdf/taniar2008.pdf. You can also view
the first chapter and preface of the publication online at
http://www.igi-global.com/books/details.asp?id=7139.
>
>
>
> ****************************************
>
> To view the full contents of this publication, check for Data Mining and
Knowledge Discovery Technologies in your institution's library. If you
library does not currently own this title, please recommend it to your
librarian.
>
> ****************************************
>
>
>
>
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Received on Wed Jan 2 07:53:39 2008