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Hierarchical Clustering
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Hierarchical clustering is a method of cluster analysis which seeks to build a hierarchy of clusters.
We apply a "bottom up" approach: each observation starts in its own clister, and pairs of clusters are subsequently merged.

The merges are determined in a greedy manner.
We start by constructing a pairwise distance matrix. Then, the clusters of the pair with closest distance are merged iteratively.

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Example
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Imagine we have files with the training data. We create CDenseFeatures (here 64 bit floats aka RealFeatures) as:

.. sgexample:: hierarchical.sg:create_features

In order to run :sgclass:`CHierarchical`, we need to choose a distance, for example :sgclass:`CEuclideanDistance`, or other sub-classes of :sgclass:`CDistance`. The distance is initialized with the data we want to classify.

.. sgexample:: hierarchical.sg:choose_distance

We then create an instance of the :sgclass:`CHierarchical` classifier by assigning the steps of merging we expect to have in the training.

.. sgexample:: hierarchical.sg:create_instance

We can extract the information of the two merged elements, as well as the distance between them in each merging step:

.. sgexample:: hierarchical.sg:extract_results

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References
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:wiki:`Hierarchical_clustering`