A Parameterless Method for Efficiently Discovering Clusters of Arbitrary Shape in Large Datasets Andrew Foss and Osmar R. Zaļane   View Paper (PDF)   Return to Main-Track Regular Papers Abstract 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 exist-ing clustering algorithms require some parameters to steer the clustering process, such as the famous k 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. DiSC'03 © 2003 Association for Computing Machinery