Automatic detection of cluster structure changes using relative density self-organizing maps

Denny, Pandu Wicaksono, Ruli Manurung

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

1 Citation (Scopus)


Knowledge of clustering changes in real-life datasets is important in many contexts, such as customer attrition analysis and fraud detection. Organizations can use such knowledge of change to adapt business strategies in response to changing circumstances. Analysts should be able to relate new knowledge acquired from a newer dataset to that acquired from an earlier dataset to understand what has changed. There are two kind of clustering changes, which are: changes in clustering structure and changes in cluster memberships. The key contribution of this paper is a novel method to automatically detect structural changes in two snapshot datasets using ReDSOM. The method identifies emerging clusters, disappearing clusters, splitting clusters, merging clusters, enlarging clusters, and shrinking clusters. Evaluation using synthetic datasets demonstrates that this method can identify automatically structural cluster changes. Moreover, the changes identified in our evaluation using real-life datasets from the World Bank can be related to actual changes.

Original languageEnglish
Title of host publicationData Mining and Analytics 2014 - Proceedings of the 12th Australasian Data Mining Conference, AusDM 2014
EditorsYanchang Zhao, Yanchang Zhao, Lin Liu, Kok-Leong Ong, Xue Li
PublisherAustralian Computer Society
Number of pages10
ISBN (Electronic)9781921770173
Publication statusPublished - 2014

Publication series

NameConferences in Research and Practice in Information Technology Series
ISSN (Print)1445-1336


  • Self-organizing maps
  • Temporal clustering
  • Visualization


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