Abstract
Cluster analysis is often used to find clusters and algorithms are designed and tuned to find the “right” clusters. Instead of searching for the “best” clustering algorithm, we argue that a clear concept of what the aim of a cluster analysis is and a better understanding of the data – especially based on visualisations – can be more crucial than the search for the right algorithm. In this paper, we revisit a method called dynamic data assigning assessment clustering that was intended both to asses the inherent cluster structure in a data set as well as to find the clusters. Here we extend this algorithm for better visualisation of possible cluster structures and also to validate single clusters that were found by other algorithms. Although this new approach can help to identify clusters, it is supporting tool and not used as a clustering algorithm itself.
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