Abstract:

This paper presents a data clustering method named BIRCH(Balanced Iterative Reducing and Clustering using Hierarchies), and demonstrate that these especially suitable for very large data bases . BIRCH icrementally and dynamically clusters incoming multy dimensional metric data points to trie to produce the best qulity clustering with available recourses (i.e., available memory and time constraints).

BIRCH can typically find a good clustering with a single-scan of the data and improve the quality further with few additional scans. It is also 1`st clustering algorithm proposed in data base area to handle "noise" effectively.(noise means data points that are not part of the underlining pattern)