Classification and Clustering for Knowledge Discovery

By: Saman K. Halgamuge (Editor), Lipo Wang (Editor)

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Hardcover | 1 October 2005

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374 Pages

Dimensions(cm)
23.5 x 16.51 x 2.54

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Knowledge Discovery today is a significant study and research area. In finding answers to many research questions in this area, the ultimate hope is that knowledge can be extracted from various forms of data around us. This book covers recent advances in unsupervised and supervised data analysis methods in Computational Intelligence for knowledge discovery. In its first part the book provides a collection of recent research on distributed clustering, self organizing maps and their recent extensions. If labeled data or data with known associations are available, we may be able to use supervised data analysis methods, such as classifying neural networks, fuzzy rule-based classifiers, and decision trees. Therefore this book presents a collection of important methods of supervised data analysis. "Classification and Clustering for Knowledge Discovery" also includes variety of applications of knowledge discovery in health, safety, commerce, mechatronics, sensor networks, and telecommunications.

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You Can Find This Book In

Non-Fiction Computing & I.T.Databases Data Mining Mathematics Applied Mathematics Graphical & Digital Media Applications Engineering & Technology Technology in General Maths for Engineers Business & Management Operational Research

Computer Science Artificial Intelligence Computer Vision Engineering in General

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Paperback

Published: 28th October 2010

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The Many Faces of a Kohonen Map	p. 1
Profiling Network Applications with Fuzzy C-means and Self-Organizing Maps	p. 15
Monitoring Shift and Movement in Data using Dynamic Feature Maps	p. 29
Serendipity in Text and Audio Information Spaces: Organizing and Exploring High-Dimensional Data with the Growing Hierarchical Self-Organizing Map	p. 43
D-GridMST: Clustering Large Distributed Spatial Databases	p. 61
A Probabilistic Approach to Mining Fuzzy Frequent Patterns	p. 73
Identifying Interesting Patterns in Multidatabases	p. 91
Comparison Between Five Classifiers for Automatic Scoring of Human Sleep Recordings	p. 113
Prioritized Fuzzy Information Fusion for Handling Multi-Criteria Fuzzy Decision-Making Problems	p. 129
Using Boosting Techniques to Improve the Performanceof Fuzzy Classification Systems	p. 147
P-Expert: Implementation and Deploymentof Large Scale Fuzzy Expert Advisory System	p. 159
Data Mining and User Profilingfor an E-Commerce System	p. 175
Soft Computing Models for Network Intrusion Detection Systems	p. 191
Use of Fuzzy Feature Descriptions to Recognize Handwritten Alphanumeric Characters	p. 209
Soft Computing Paradigmsfor Web Access Pattern Analysis	p. 233
Discovery of Fuzzy Multiple-Level Web Browsing Patterns	p. 251
Ontology-based Fuzzy Decision Agent and Its Applicationto Meeting Scheduling Support System	p. 267
A Longitudinal Comparison of Supervised and Unsupervised Learning Approaches to Iso-Resource Grouping for Acute Healthcare in Australia	p. 283
Data Mining of Missing Persons Data	p. 305
Centralised Strategies for Cluster Formationin Sensor Networks	p. 315
Adaptive Fuzzy Zone Routingfor Wireless Ad Hoc Networks	p. 333
A New Approach to Building Fuzzy Classificationsin Sociological Research with Survey Data	p. 349
Table of Contents provided by Publisher. All Rights Reserved.

Classification and Clustering for Knowledge Discovery

At a Glance

Hardcover

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How to return your order

Defective items

You Can Find This Book In

Other Editions and Formats

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