Visual Assessment of Cluster Tendency with Variations of Distance Measures

Finding the cluster structure is essential for analyzing self-organized networking structures, such as social networks. In such problems, a wide variety of distance measures can be used. Common clustering methods often require the number of clusters to be explicitly indicated before starting the process of clustering. A preliminary step to clustering is deciding, firstly, whether the data contain any clusters and, secondly, how many clusters the dataset contains. To highlight the internal structure of data, several methods for visual assessment of clustering tendency (VAT family of methods) have been developed. The vast majority of these methods use the Euclidean distance or cosine similarity measure. In our study, we modified the VAT and iVAT algorithms for visual assessment of the clustering tendency with a wide variety of distance measures. We compared the results of our algorithms obtained from both samples from repositories and data from applied problems.