BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) is used to perform hierarchical clustering over particularly large data-sets.
With modifications it can also be used to accelerate k-means clustering and Gaussian mixture modeling with the expectation–maximization algorithm.
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 And this can lead to fast working cluster
- https://laptrinhx.com/birch-clustering-clearly-explained-1946056246/
- https://www.geeksforgeeks.org/ml-birch-clustering/
- https://www.pallavikulkarni.in/incremental-clustering-with-birch-algorithm/
- https://github.com/annoviko/pyclustering/blob/master/pyclustering/cluster/birch.py
- https://en.wikipedia.org/wiki/BIRCH
- https://www.datatechnotes.com/2019/09/clustering-example-with-birch-method-in.html
- https://morioh.com/p/c23e0d680669