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# scipy.cluster.hierarchy.leaders

`scipy.cluster.hierarchy.``leaders`(Z, T)

  (L, M) = leaders(Z, T):

  Returns the root nodes in a hierarchical clustering corresponding to a cut defined by a flat cluster assignment vector `T`. See the `fcluster` function for more information on the format of `T`.

  For each flat cluster of the flat clusters represented in the n-sized flat cluster assignment vector `T`, this function finds the lowest cluster node in the linkage tree Z such that:

  > - leaf descendents belong only to flat cluster j (i.e. `T[p]==j` for all in where is the set of leaf ids of leaf nodes descendent with cluster node )
  > - there does not exist a leaf that is not descendent with that also belongs to cluster (i.e. `T[q]!=j` for all not in ). If this condition is violated, `T` is not a valid cluster assignment vector, and an exception will be thrown.

  Arguments : | Z : ndarray The hierarchical clustering encoded as a matrix. See `linkage` for more information. T : ndarray The flat cluster assignment vector.
  Returns : | (L, M) L : ndarray The leader linkage node id’s stored as a k-element 1D array where is the number of flat clusters found in `T`. `L[j]=i` is the linkage cluster node id that is the leader of flat cluster with id M[j]. If `i < n`, `i` corresponds to an original observation, otherwise it corresponds to a non-singleton cluster. For example: if `L[3]=2` and `M[3]=8`, the flat cluster with id 8’s leader is linkage node 2. M : ndarray The leader linkage node id’s stored as a k-element 1D array where is the number of flat clusters found in `T`. This allows the set of flat cluster ids to be any arbitrary set of integers.

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- SciPy v0.9 Reference Guide (DRAFT) »
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