Applying dynamic self organizing maps for identifying changes in data sequences

Authors:
Rasika Amarasiri;Damminda Alahakoon
Affiliations:
School of Business Systems, Monash University, Australia;School of Business Systems, Monash University, Australia
Venue:
Design and application of hybrid intelligent systems
Year:
2003

Citing 5
Cited 0

Self-organization and associative memory: 3rd edition

Self-organization and associative memory: 3rd edition
Mining a growing feature map by data skeleton modelling

Data mining and computational intelligence
Self-Organizing Maps

Self-Organizing Maps
Discovering Web Access Patterns and Trends by Applying OLAP and Data Mining Technology on Web Logs

ADL '98 Proceedings of the Advances in Digital Libraries Conference
Dynamic self-organizing maps with controlled growth for knowledge discovery

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

The Growing Self Organizing Map (GSOM), a variant of the Self Organizing Map has been mainly used to cluster and identify relationships in static data in an unsupervised manner. In this paper we discuss about the capabilities of the GSOM in identifying changes in data sequences. To illustrate this we use the analysis of a web server log using the GSOM and highlight the advantages of using the GSOM over traditional web-log analysis methods.