On non-euclidean metrics based clustering

Authors:
Hong Cao;Ping Wang;Runing Ma;Jundi Ding
Affiliations:
School of Science, Nanjing University of Aeronautics and Astronautics, Nanjing, China;School of Science, Nanjing University of Aeronautics and Astronautics, Nanjing, China;School of Science, Nanjing University of Aeronautics and Astronautics, Nanjing, China;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China
Venue:
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Year:
2012

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Abstract

In this paper, non-Euclidean metrics, such as kernel metric, Mahalanobis distance and the metric based on the shortest weighted path, are introduced into PAM and CURE clustering algorithms. The purpose is to have a detailed research on non-Euclidean metrics based clustering. Firstly, modified algorithms are established by replacing Euclidean metric with non-Euclidean metrics. Then these modified algorithms are applied on various data sets including UCI data sets as well as artificial data sets. Detailed evaluations and analysis have been made about the performances of different metrics. Experimental results demonstrate that the application scope of these clustering algorithms has been extended by adopting non-Euclidean metrics. As a result, we can conclude that the application of non-Euclidean metrics is of great importance.