A visual approach for external cluster validation

Ke Bing Zhang*, Mehmet A. Orgun, Kang Zhang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

5 Citations (Scopus)
8 Downloads (Pure)


Visualization can be very powerful in revealing cluster structures. However, directly using visualization techniques to verify the validity of clustering results is still a challenge. This is due to the fact that visual representation lacks precision in contrasting clustering results. To remedy this problem, in this paper we propose a novel approach, which employs a visualization technique called HOV (Hypothesis Oriented Verification and Validation by Visualization) which offers a tunable measure mechanism to project clustered subsets and non-clustered subsets from a multidimensional space to a 2D plane. By comparing the data distributions of the subsets, users not only have an intuitive visual evaluation but also have a precise evaluation on the consistency of cluster structure by calculating geometrical information of their data distributions.

Original languageEnglish
Title of host publicationProceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007
EditorsWlodek Duch, Joydeep Ghosh
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages7
ISBN (Print)1424407052, 9781424407057
Publication statusPublished - 2007
Event1st IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007 - Honolulu, HI, United States
Duration: 1 Apr 20075 Apr 2007


Other1st IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007
CountryUnited States
CityHonolulu, HI

Bibliographical note

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