Visualization of cluster changes by comparing self-organizing maps

Denny, David McG Squire

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Citations (Scopus)

Abstract

In this paper we introduce Self-Organizing Map-based techniques that can reveal structural cluster changes in two related data sets from different time periods in a way that can explain the new result in relation to the previous one. These techniques are demonstrated using a real-world data set from the World Development Indicators database maintained by the World Bank. The results verify that the methods are capable of revealing changes in cluster strucure and membership, corresponding to known changes in economic fortunes of countries.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages410-419
Number of pages10
Publication statusPublished - 1 Dec 2005
Event9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2005 - Hanoi, Viet Nam
Duration: 18 May 200520 May 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3518 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2005
Country/TerritoryViet Nam
CityHanoi
Period18/05/0520/05/05

Fingerprint

Dive into the research topics of 'Visualization of cluster changes by comparing self-organizing maps'. Together they form a unique fingerprint.

Cite this