Experimental study on dominant sets clustering

Based on a graph-theoretic concept of a cluster, dominant sets clustering has been shown to be an attractive clustering algorithm with many useful properties. In this study, the authors conduct a comprehensive study of related issues in dominant sets clustering, in an endeavour to explore the potential of this algorithm and obtain the best clustering results. Specifically, they empirically investigate how similarity parameters, similarity measures and game dynamics influence the dominant sets clustering results. From experiments on eight datasets, they conclude that distance-based similarity measures perform evidently better than cosine and histogram intersection similarity measures potentially, and they need to find the best-performing similarity parameter to make use of this advantage. They then study the effect of similarity parameter on dominant sets clustering results and induce the range of the best-performing similarity parameters. Furthermore, they find that the recently proposed infection and immunisation dynamics performs better than the replicator dynamics in most cases while being much more efficient than the latter. These observations are helpful in applying dominant sets clustering to practical problems, and also indicate directions for further improvement of this algorithm.