On a Minimal Spanning, Tree Approach in the Cluster Validation Problem

Date

2009-01-01

Author

Barzily, Zeev
Volkovich, Zeev
Öztürk, Başak
Weber, Gerhard Wilhelm

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In this paper, a method for the study of cluster stability is purposed. We draw pairs of samples from the data, according to two sampling distributions. The first distribution corresponds to the high density zones of data-elements distribution. Thus it is associated with the clusters cores. The second one, associated with file cluster margins, is related to the low density zones. The samples are clustered and the two obtained partitions are compared. The partitions are considered to be consistent if the obtained clusters are similar. The resemblance is measured by the total number of edges, in the clusters minimal spanning trees, connecting points from different samples. We use the Friedman and Rafsky two sample test Statistic. Under the homogeneity hypothesis, this statistic is normally distributed. Thus, it can he expected that the true number of clusters corresponds to the statistic empirical distribution which is closest to normal. Numerical experiments demonstrate the ability of the approach to detect the true number of clusters.

Subject Keywords

Two sample test, Minimal spanning tree, Cluster validation, Clustering

URI

https://hdl.handle.net/11511/54010

Journal

INFORMATICA

Collections

Department of Industrial Engineering, Article

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Citation Formats

Z. Barzily, Z. Volkovich, B. Öztürk, and G. W. Weber, “On a Minimal Spanning, Tree Approach in the Cluster Validation Problem,” INFORMATICA, pp. 187–202, 2009, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/54010.