A prediction-based visual approach for cluster exploration and cluster validation by HOV

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

*Corresponding author for this work

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

9 Citations (Scopus)

Abstract

Predictive knowledge discovery is an important knowledge acquisition method. It is also used in the clustering process of data mining. Visualization is very helpful for high dimensional data analysis, but not precise and this limits its usability in quantitative cluster analysis. In this paper, we adopt a visual technique called HOV3 to explore and verify clustering results with quantified measurements. With the quantified contrast between grouped data distributions produced by HOV3, users can detect clusters and verify their validity efficiently.

Original languageEnglish
Title of host publicationKnowledge Discovery in Database: PKDD 2007 - 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings
EditorsJoost N. Kok, Jacek Koronacki, Ramon Lopez de Mantaras, Stan Matwin, Dunja Mladenic, Andrzej Skowron
Place of PublicationBerlin; Heidelberg
PublisherSpringer, Springer Nature
Pages336-349
Number of pages14
Volume4702 LNAI
ISBN (Print)9783540749752
Publication statusPublished - 2007
Event11th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2007 - Warsaw, Poland
Duration: 17 Sep 200721 Sep 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4702 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other11th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2007
CountryPoland
CityWarsaw
Period17/09/0721/09/07

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  • Cite this

    Zhang, K. B., Orgun, M. A., & Zhang, K. (2007). A prediction-based visual approach for cluster exploration and cluster validation by HOV. In J. N. Kok, J. Koronacki, R. L. de Mantaras, S. Matwin, D. Mladenic, & A. Skowron (Eds.), Knowledge Discovery in Database: PKDD 2007 - 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings (Vol. 4702 LNAI, pp. 336-349). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4702 LNAI). Berlin; Heidelberg: Springer, Springer Nature.