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Hello and welcome to this art tutorial.

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So in the previous tutorial,
we plotted our dendrogram

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to find the optimal number of clusters
and the result was five clusters.

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So now in this new step,
since we have this

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optimal number of clusters,
we are going to fit the hierarchical

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clustering algorithm to our data X
with obviously five clusters.

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Okay.

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So the first thing that we need to do is
to create an object of the H class class.

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And actually
this is something that we already did.

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Because if we go back to step two here
when we wrote dendrogram

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equals h class, then the distance matrix
and the word method

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we actually created an object of the
H class class.

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And we're going to do the exact same thing
right now

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we're going to create the exact
same object from the H class class.

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So I'm going to copy this line of code,

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paste it here in step three.

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And I'm just going to change
the name of the object

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just to make things nice and clear.

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So let's replace here dendrogram by HC.

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And now we have the same object as before
only with a different name.

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And this actually makes sense
because by building a dendrogram

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we actually do all the steps
of hierarchical clustering.

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And among all these steps
there is the step in which

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the algorithm found five clusters.

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So that means that this object
contains the information

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when we have five clusters.

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And so now let's use this object
to build our vector of clusters.

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That is the vector that will tell us
which cluster each customer belongs to.

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And to build this vector
we're going to use one of the method

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of the H class class
which is the cut tree method.

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So we're going to call our vector
of clusters y underscore hc equals.

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Then we use the country
method of the h class class.

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So let's type cut tree and then press f1.

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And then here

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we see that this method requires
three arguments.

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The first argument is tree.

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And of course it's our dendrogram
that we just renamed HC.

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Then the second argument
is k the number of clusters.

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So here we input five

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and we will leave the default value
for the third parameter.

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Okay.

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Now it's interesting to take a step back
because actually this method is called

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cut tree which is a very well chosen name
because indeed we are cutting the tree

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to take the part of the tree
that contains our five clusters.

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So that makes pretty much sense.

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And now, since we are actually done
with fitting hierarchical clustering

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to our data set, let's select
this code section here and execute

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to find our y hc vector of clusters
that appears right here.

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And now if we

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type in the console y hc here
and press enter, we have all the clusters

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that the hierarchical clustering algorithm
assigned to each customer.

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So for example customer
number one belongs to cluster one.

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And customer
number two belongs to cluster two.

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Customer number 106
belongs to cluster three.

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And our last customer
belongs to cluster four.

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Okay.

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So congratulations
we found the right number of clusters.

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And we fit it correctly to hierarchical
clustering to our model data set.

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And now time for fun. In the next tutorial

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we will be visualizing
a hierarchical clustering results.