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BIRCH (balanced iterative reducing and clustering using hierarchies) is an unsupervised data mining algorithm used to perform hierarchical clustering over particularly large data-sets.^[1] With modifications it can also be used to accelerate k-means clustering and Gaussian mixture modeling with the expectation–maximization algorithm.^[2] 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.

Its inventors claim BIRCH to be the "first clustering algorithm proposed in the database area to handle 'noise' (data points that are not part of the underlying pattern) effectively",^[1] beating DBSCAN by two months. The BIRCH algorithm received the SIGMOD 10 year test of time award in 2006.^[3]

^ ^a ^b Zhang, T.; Ramakrishnan, R.; Livny, M. (1996). "BIRCH: an efficient data clustering method for very large databases". Proceedings of the 1996 ACM SIGMOD international conference on Management of data - SIGMOD '96. pp. 103–114. doi:10.1145/233269.233324.
^ Cite error: The named reference :0 was invoked but never defined (see the help page).
^ "2006 SIGMOD Test of Time Award". Archived from the original on 2010-05-23.