The discovery of hierarchical cluster structures assisted by a visualization technique

Ke Bing Zhang*, Mehmet A. Orgun, Yanchang Zhao, Abhaya C. Nayak

*Corresponding author for this work

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

Abstract

Hierarchical clustering is very versatile in real world applications. However, due to the issue of higher computational complexity from which automated hierarchical clustering algorithms suffer, the user can hardly correct possible misclassifications from the tree-structured nature of clusters. Visualization is a powerful technique for data analysis, however, most of the existing cluster visualization techniques are mainly used for displaying clustering results. In order for the user to be directly involved in the process of discovering nested cluster structures, we introduce a visualization technique, called HOV3, to detect clusters and their internal cluster structure. As a result, our approach provides the user an effective method for the discovery of nested cluster structures by visualization.

Original languageEnglish
Title of host publicationNeural Information Processing: Theory and Algorithms - 17th International Conference, ICONIP 2010, Proceedings
EditorsKok Wai Wong, B. Mendis, U. Sumudu, Abdesselam Bouzerdoum
Place of PublicationBerlin
PublisherSpringer, Springer Nature
Pages703-711
Number of pages9
Volume6443 LNCS
EditionPART 1
ISBN (Print)3642175368, 9783642175367
DOIs
Publication statusPublished - 2010
Event17th International Conference on Neural Information Processing, ICONIP 2010 - Sydney, NSW, Australia
Duration: 22 Nov 201025 Nov 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6443 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other17th International Conference on Neural Information Processing, ICONIP 2010
CountryAustralia
CitySydney, NSW
Period22/11/1025/11/10

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