HOV3: An approach to visual cluster analysis

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

*Corresponding author for this work

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

6 Citations (Scopus)


Clustering is a major technique in data mining. However the numerical feedback of clustering algorithms is difficult for user to have an intuitive overview of the dataset that they deal with. Visualization has been proven to be very helpful for high-dimensional data analysis. Therefore it is desirable to introduce visualization techniques with user's domain knowledge into clustering process. Whereas most existing visualization techniques used in clustering are exploration oriented. Inevitably, they are mainly stochastic and subjective in nature. In this paper, we introduce an approach called HOV3 (Hypothesis Oriented Verification and Validation by Visualization), which projects high-dimensional data on the 2D space and reflects data distribution based on user hypotheses. In addition, HOV3 enables user to adjust hypotheses iteratively in order to obtain an optimized view. As a result, HOV3 provides user an efficient and effective visualization method to explore cluster information.

Original languageEnglish
Title of host publicationAdvanced Data Mining and Applications - Second International Conference, ADMA 2006, Proceedings
EditorsXue Li, Osmar R. Zane, Zhanhuai Li
Place of PublicationBerlin; Heidelberg
PublisherSpringer, Springer Nature
Number of pages12
Volume4093 LNAI
ISBN (Electronic)9783540370253
ISBN (Print)3540370250, 9783540370253
Publication statusPublished - 2006
Event2nd International Conference on Advanced Data Mining and Applications, ADMA 2006 - Xi'an, China
Duration: 14 Aug 200616 Aug 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4093 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349


Other2nd International Conference on Advanced Data Mining and Applications, ADMA 2006


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