A Parameterless Method for Efficiently Discovering Clusters of Arbitrary Shape in Large Datasets

Clustering is the problem of grouping data based on similarity and consists of maximizing the intra-group similarity while minimizing the inter-group similarity. The problem of clustering data sets is also known as unsupervised classification, since no class labels are given. However, all existing clustering algorithms require some parameters to steer the clustering process, such as the famous for the number of expected clusters, which constitutes a supervision of a sort. We present in this paper a new, efficient, fast and scalable clustering algorithm that clusters over a range of resolutions and finds a potential optimum clustering without requiring any parameter input. Our experiments show that our algorithm outperforms most existing clustering algorithms in quality and speed for large data sets.