DBSCAN

  BIRCH (balanced iterative reducing and clustering using hierarchies) is an unsupervised data mining algorithm used to perform hierarchical clustering over particularly large data-sets. An advantage of Birch is its ability to incrementally and dynamically cluster incoming, multi-dimensional metric data points in an attempt to produce the best quality clustering for a given set of resources (memory and time constraints). In most cases, Birch only requires a single scan of the database. In addition, Birch is recognized[1] as the, "first clustering algorithm proposed in the database area to handle noise (data points that are not part of the underlying pattern) effectively".