The discovery of hierarchical cluster structures assisted by a visualization technique

Authors:
Ke-Bing Zhang;Mehmet A. Orgun;Yanchang Zhao;Abhaya C. Nayak
Affiliations:
Department of Computing, Macquarie University, Sydney, NSW, Australia;Department of Computing, Macquarie University, Sydney, NSW, Australia;Centrelink Australia;Department of Computing, Macquarie University, Sydney, NSW, Australia
Venue:
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Year:
2010

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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.