Published by InTech. Janeza Trdine 9, 51000 Rijeka, Croatia. 2009. ISBN 978-3-902613-53-0
436 p.
Первоисточник: http://www.intechopen.com.
This book presents four different ways of theoretical and practical advances and applications of data mining in different promising areas like Industrialist, Biological, and Social. Twenty six chapters cover different special topics with proposed novel ideas. Each chapter gives an overview of the subjects and some of the chapters have cases with offered data mining solutions. We hope that this book will be a useful aid in showing a right way for the students, researchers and practitioners in their studies.
Contents:
A data mining & knowledge discovery process model
Knowledge discovery on the grid
Rough set theory — fundamental concepts, principals, data extraction, and applications
Robust data mining: an integrated approach
On the selection of meaningful association rules
Hybrid clustering for validation and improvement of subject-classification schemes
Automatic product classification control system using RFID tag information and data mining
Hyperspectral remote sensing data mining using multiple classifiers combination
Content-based image classification via visual learning
Clustering parallel data streams
Mining multiple-level association rules based on pre-large concepts
Data mining applications: promise and challenges
Mining spatio-temporal datasets: relevance, challenges and current research directions
Benchmarking the data mining algorithms with adaptive neuro-fuzzy inference system in GSM churn management
Using data mining to investigate the behavior of video rental customers
A novel model for global customer retention using data mining technology
Data mining in web applications
Application of data mining techniques to the data analyses to ensure safety of medicine usage
Data mining in the molecular biology era — a study directed to carbohydrates biosynthesis and accumulation in plants
Microarray data mining for biological pathway analysis
Development of microsatellite markers by data mining from DNA sequences
Quality improvement using data mining in manufacturing processes
The deployment of data mining into operational business processes
Data mining applied to the instrumentation data analysis of a large dam
A data mining algorithm for monitoring PCB assembly quality
An overview of data mining techniques applied to power systems
