Visualizing temporal cluster changes using Relative Density Self-Organizing Maps

Denny, Graham J. Williams, Peter Christen

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)

Abstract

We introduce a Self-Organizing Map (SOM)-based visualization method that compares cluster structures in temporal datasets using Relative Density SOM (ReDSOM) visualization. ReDSOM visualizations combined with distance matrix-based visualizations and cluster color linking, is capable of visually identifying emerging clusters, disappearing clusters, split clusters, merged clusters, enlarging clusters, contracting clusters, the shifting of cluster centroids, and changes in cluster density. As an example, when a region in a SOM becomes significantly more dense compared to an earlier SOM, and is well separated from other regions, then the new region can be said to represent a new cluster. The capabilities of ReDSOM are demonstrated using synthetic datasets, as well as real-life datasets from the World Bank and the Australian Taxation Office. The results on the real-life datasets demonstrate that changes identified interactively can be related to actual changes. The identification of such cluster changes is important in many contexts, including the exploration of changes in population behavior in the context of compliance and fraud in taxation.

Original languageEnglish
Pages (from-to)281-302
Number of pages22
JournalKnowledge and Information Systems
Volume25
Issue number2
DOIs
Publication statusPublished - 1 Nov 2010

Keywords

  • Change analysis
  • Hot spots analysis
  • Self-Organizing Map
  • Temporal cluster analysis
  • Visual data exploration

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