![]() In this way, if a cluster at a specific tree level contains a number of samples higher than the given maximum, it is ignored and its offspring (smaller) sub-clusters are taken into consideration. the clusters with only one sample), retrieving the biggest possible clusters containing a number of samples lower than a given maximum. The proposed function looks recursively along the hierarchical tree, from the root (single cluster gathering all the samples) to the leaves (i.e. it contains clusters of very variable size. Thus, the resulting clustering is unbalanced, i.e. ![]() the result from a hierarchical clustering), probably you will end up having a few big clusters (where the number of data samples is high), and many small clusters (each containing very few data samples). The initial problem was the following: if you perform a standard cut on a tree (i.e. It builds upon the SciPy and NumPy libraries. This package contains a Python function that performs a balanced cut tree of a SciPy linkage matrix built using any linkage method (e.g. ![]() ![]() Balanced Cut Tree Method for Hierarchical Clustering
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