Classification and Clustering for Knowledge Discovery

✍ Scribed by V. Lemaire, F. Clérot (auth.), Dr. Saman K. Halgamuge, Dr. Lipo Wang (eds.)

• Publisher: Springer-Verlag Berlin Heidelberg
• Year: 2005
• Tongue: English
• Leaves: 355
• Series: Studies in Computational Intelligence 4
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

The Many Faces of a Kohonen Map A Case Study: SOM-based Clustering for On-Line Fraud Behavior Classification....Pages 1-13
Profiling Network Applications with Fuzzy C-means and Self-Organizing Maps....Pages 15-27
Monitoring Shift and Movement in Data using Dynamic Feature Maps....Pages 29-41
Serendipity in Text and Audio Information Spaces: Organizing and Exploring High-Dimensional Data with the Growing Hierarchical Self-Organizing Map....Pages 43-60
D-GridMST: Clustering Large Distributed Spatial Databases....Pages 61-72
A Probabilistic Approach to Mining Fuzzy Frequent Patterns....Pages 73-89
Identifying Interesting Patterns in Multidatabases....Pages 91-112
Comparison Between Five Classifiers for Automatic Scoring of Human Sleep Recordings....Pages 113-127
Prioritized Fuzzy Information Fusion for Handling Multi-Criteria Fuzzy Decision-Making Problems....Pages 129-145
Using Boosting Techniques to Improve the Performance of Fuzzy Classification Systems....Pages 147-157
P-Expert: Implementation and Deployment of Large Scale Fuzzy Expert Advisory System....Pages 159-173
Data Mining and User Profiling for an E-Commerce System....Pages 175-189
Soft Computing Models for Network Intrusion Detection Systems....Pages 191-207
Use of Fuzzy Feature Descriptions to Recognize Handwritten Alphanumeric Characters....Pages 209-232
Soft Computing Paradigms for Web Access Pattern Analysis....Pages 233-250
Discovery of Fuzzy Multiple-Level Web Browsing Patterns....Pages 251-266
Ontology-based Fuzzy Decision Agent and Its Application to Meeting Scheduling Support System....Pages 267-282
A Longitudinal Comparison of Supervised and Unsupervised Learning Approaches to Iso-Resource Grouping for Acute Healthcare in Australia....Pages 283-303
Data Mining of Missing Persons Data....Pages 305-314
Centralised Strategies for Cluster Formation in Sensor Networks....Pages 315-331
Adaptive Fuzzy Zone Routing for Wireless Ad Hoc Networks....Pages 333-347
A New Approach to Building Fuzzy Classifications in Sociological Research with Survey Data....Pages 349-356

✦ Subjects

Appl.Mathematics/Computational Methods of Engineering; Artificial Intelligence (incl. Robotics); Computer Imaging, Vision, Pattern Recognition and Graphics; Computer-Aided Engineering (CAD, CAE) and Design; Applications of Mathematics; O