A visual method for high-dimensional data cluster exploration

Ke Bing Zhang*, Mao Lin Huang, Mehmet A. Orgun, Quang Vinh Nguyen

*Corresponding author for this work

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

3 Citations (Scopus)


Visualization is helpful for clustering high dimensional data. The goals of visualization in data mining are exploration, confirmation and presentation of the clustering results. However, the most of visual techniques developed for cluster analysis are primarily focused on cluster presentation rather than cluster exploration. Several techniques have been proposed to explore cluster information by visualization, but most of them depend heavily on the individual user's experience. Inevitably, this incurs subjectivity and randomness in the clustering process. In this paper, we employ the statistical features of datasets as predictions to estimate the number of clusters by a visual technique called HOV3. This approach mitigates the problem of the randomness and subjectivity of the user during the process of cluster exploration by other visual techniques. As a result, our approach provides an effective visual method for cluster exploration.

Original languageEnglish
Title of host publicationNeural Information Processing - 16th International Conference, ICONIP 2009, Proceedings
EditorsChi Sing Leung, Minho Lee, Jonathan H. Chan
Place of PublicationBerlin, Germany
PublisherSpringer, Springer Nature
Number of pages11
Volume5864 LNCS
EditionPART 2
ISBN (Print)364210682X, 9783642106828
Publication statusPublished - 2009
Event16th International Conference on Neural Information Processing, ICONIP 2009 - Bangkok, Thailand
Duration: 1 Dec 20095 Dec 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume5864 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349


Other16th International Conference on Neural Information Processing, ICONIP 2009


Dive into the research topics of 'A visual method for high-dimensional data cluster exploration'. Together they form a unique fingerprint.

Cite this