Hypothesis oriented cluster analysis in data mining by visualization

Authors:
Ke-Bing Zhang;Mehmet A. Orgun;Kang Zhang;Yihao Zhang
Affiliations:
Macquarie University, Sydney, NSW, Australia;Macquarie University, Sydney, NSW, Australia;The University of Texas at Dallas, Richardson, TX;Macquarie University, Sydney, NSW, Australia
Venue:
Proceedings of the working conference on Advanced visual interfaces
Year:
2006

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Abstract

Cluster analysis is an important technique that has been used in data mining. However, cluster analysis provides numerical feedback making it hard for users to understand the results better; and also most of the clustering algorithms are not suitable for dealing with arbitrarily shaped data distributions of datasets. While visualization techniques have been proven to be effective in data mining, their use in cluster analysis is still a major challenge, especially in data mining applications with high-dimensional and huge datasets. This paper introduces a novel approach, Hypothesis Oriented Verification and Validation by Visualization, named HOV3, which projects datasets based on given hypotheses by visualization in 2D space. Since HOV3 approach is more goal-oriented, it can assist the user in discovering more precise cluster information from high-dimensional datasets efficiently and effectively.