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In a standard cluster analysis, such as k-means, in addition to clusterslocations and distances between them, it's important to know if they areconnected or well separated from each other. The main focus of this paper isdiscovering the relations between the resulting clusters. We propose a newmethod which is based on pairwise overlapping k-means clustering, that inaddition to means of clusters provides the graph structure of their relations.The proposed method has a set of parameters that can be tuned in order tocontrol the sensitivity of the model and the desired relative size of thepairwise overlapping interval between means of two adjacent clusters, i.e.,level of overlapping. We present the exact formula for calculating thatparameter. The empirical study presented in the paper demonstrates that ourapproach works well not only on toy data but also compliments standardclustering results with a reasonable graph structure on real datasets, such asfinancial indices and restaurants.

Discovering the Graph Structure in the Clustering Results