Name: K Means Clustering: Pros and Cons of K Means Clustering
Uploaded: 2020-03-09T00:41:20.000Z
Duration: 24 min 7 s
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In cluster analysis, that’s how a cluster would look in two-dimensional space.

There are two dimensions, or two features based on which we are performing clustering.

For instance, the ‘Age’ and ‘Money spent’.

Certainly, it makes no sense to have only one cluster, so let me zoom out of this graph.

If we want to identify three clusters, this is the result we obtain.

And that’s more or less how clustering works graphically.

How do we perform clustering in practice?

There are different methods we can apply to identify clusters.

The most popular one is k-means, so that’s where we will start.

Let’s simplify this scatter to 15 points, so we can get a better grasp of what happens.

First, we must choose how many clusters we’d like to have.

That’s where this method gets its name from.

K stands for the number of clusters we are trying to identify.

The next step is to specify the cluster seeds.

It is chosen at random or is specified by the data scientist based on prior knowledge

One of the clusters will be the green cluster, the other one the orange cluster.

The following step is to assign each point on the graph to a seed.

For instance, this point is closer to the green seed than to the orange one.

Therefore, it will belong to the green cluster.

This point, on the other hand, is closer to the orange seed, therefore, it will be a part

In this way, we can color all points on the graph, based on their Euclidean distance from

The final step is to calculate the centroid of the green points and the orange points.

The green seed will move closer to the green points to become their centroid and the orange

From here, we would repeat the last two steps.

All the green points are obviously closer to the green centroid, and the orange points

Both of them are closer to the green centroid, so at this step we will reassign them to the

Finally, we must recalculate the centroids.

Now, all the green points are closest to the green centroid and all the orange ones to

We can no longer reassign points, which completes the clustering process.

So that’s the whole idea behind clustering?

In order to solidify your understanding, we will redo the process.

In the beginning we said that with k-means clustering, we must specify the number of

The first step involves selecting the seeds.

Next, we must associate each of the points with the closest seed.

Finally, we calculate the centroids of the colored points.

We already know that k-means is an iterative process.

So, we go back to the step where we associate each of the points with the closest seed.

All orange points are settled, so no movement there.

Now they are closer to the red seed, so they will go into the red cluster.

That’s the only change in the whole graph.

In the end, we recalculate the centroids and reach a situation where no more adjustments

are necessary using the k-means algorithm.

We have reached a three-cluster solution.

This is the exact algorithm, which was used to find the solution of the problem you saw

Here’s a Python generated graph, with the three clusters colored.

I am sorry they are not the same, but you get the point.

That’s how we would usually represent the clusters graphically.

I think we have a good basis to start coding!

We are going to cluster these countries using k-means in Python.

Plus, we’ll learn a couple of nice tricks along the way.

They are pandas, NumPy, MatPlotLib, dot, PyPlot, and Seaborn.

As usual, I will set the style of all graphs to the Seaborn one.

In this course, we will rely on scikit-learn for the actual clustering.

Let’s import k-means from SK learn, dot, cluster.

Note that both the ‘K’ and the ‘M’ in k-means are capital.

Next, we will create a variable called ‘data’, where we will load the CSV file: ‘3.01.

We’ve got Country, Latitude, Longitude, and Language.

What about the latitude and longitude values?

These entries correspond to the geographic centers of the countries in our dataset.

If you Google: ‘geographic center of US’, you’ll get a Wikipedia article, indicating

it to be some point in South Dakota with a latitude of 44 degrees and 58 minutes North,

and a longitude of 103 degrees and 46 minutes West.