BIRCH, short for balanced iterative reducing and clustering using hierarchies, is an unsupervised data-mining algorithm for performing hierarchical clustering over particularly large data sets, and with modification it can also accelerate k-means clustering and Gaussian mixture modeling through the expectation-maximization algorithm. It can incrementally and dynamically cluster incoming multi-dimensional data points to produce the best-quality clustering achievable within given memory and time constraints, and in most cases requires only a single scan of the database. Its inventors describe BIRCH as the first clustering algorithm proposed in the database field to handle noisy data points effectively, and the algorithm received the SIGMOD test-of-time award in 2006.
Connections
Held Differently
DBSCAN Algorithm, Algorithms Not a settled lineage claim: BIRCH's own inventors claim priority over DBSCAN as the first database-area clustering algorithm to handle noise effectively. Verified en.wikipedia.org/wiki/BIRCH: "Its inventors claim BIRCH to be the 'first clustering algorithm proposed in the database area to handle noise... effectively', beating DBSCAN by two months."
In Field
Sources
BIRCH (Wikipedia)
In Field: Data Mining, Lead
BIRCH (balanced iterative reducing and clustering using hierarchies) is an unsupervised data mining algorithm used to perform hierarchical clustering over particularly large data-sets.
In Group: Clustering Algorithms, Lead
BIRCH (balanced iterative reducing and clustering using hierarchies) is an unsupervised data mining algorithm used to perform hierarchical clustering over particularly large data-sets.
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