Enhanced visual separation of clusters by M-mapping to facilitate cluster analysis

Authors:
Ke-Bing Zhang;Mehmet A. Orgun;Kang Zhang
Affiliations:
Department of Computing, Macquarie University, Sydney, NSW, Australia;Department of Computing, Macquarie University, Sydney, NSW, Australia;Department of Computer Science, University of Texas at Dallas, Richardson, TX
Venue:
VISUAL'07 Proceedings of the 9th international conference on Advances in visual information systems
Year:
2007

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Abstract

The goal of clustering in data mining is to distinguish objects into partitions/ clusters based on given criteria. Visualization methods and techniques may provide users an intuitively appealing interpretation of cluster structures. Having good visually separated groups of the studied data is beneficial for detecting cluster information as well as refining the membership formation of clusters. In this paper, we propose a novel visual approach called M-mapping, based on the projection technique of HOV3 to achieve the separation of cluster structures. With M-mapping, users can explore visual cluster clues intuitively and validate clusters effectively by matching the geometrical distributions of clustered and non-clustered subsets produced in HOV3.