Clustering
==========

.. highlight:: cpp



.. index:: cv::flann::hierarchicalClustering<ET,DT>


cv::cv::flann::hierarchicalClustering<ET,DT>
--------------------------------------------

`id=0.0719795489003 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/flann/cv%3A%3Aflann%3A%3AhierarchicalClustering%3CET%2CDT%3E>`__




.. cfunction:: int hierarchicalClustering<ET,DT>(const Mat\& features, Mat\& centers,                                      const KMeansIndexParams\& params)

    Clusters the given points by constructing a hierarchical k-means tree and choosing a cut in the tree that minimizes the cluster's variance.




    
    :param features: The points to be clustered. The matrix must have elements of type ET. 
    
    
    :param centers: The centers of the clusters obtained. The matrix must have type DT. The number of rows in this matrix represents the number of clusters desired, however, because of the way the cut in the hierarchical tree is chosen, the number of clusters computed will be the highest number of the form  ``(branching-1)*k+1``  that's lower than the number of clusters desired, where  ``branching``  is the tree's branching factor (see description of the KMeansIndexParams). 
    
    
    :param params: Parameters used in the construction of the hierarchical k-means tree 
    
    
    
The function returns the number of clusters computed.