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Clustering is probably one of the most common use
cases of unsupervised learning, is the task of identifying
similar instances with shared attributes in in a dataset,
and group them together into clusters.
Grouping a set of objects in such a way
that objects in the same group are more similar
to each other than those in other groups.
The output of the algorithm will be a set of labels,
assigning each data point to
one of the identified clusters.
Take a look at this graph which represents many
data points we have in an unlabeled dataset.
We can easily see that some points are denser in
specific areas, meaning they may have something in common.
This is where clustering algorithms can do their job.
They can use the input features of the dataset
and automatically assign each data points to a cluster.
Let's color the data points in
three different colors red, green, blue.
In that scenario, the clustering algorithm will find
those three clusters and automatically label them with
something that is called cluster ID,
cluster number 1, cluster number 2, cluster number 3.
So after clustering, each cluster is assigned a unique
number called this cluster ID and each data point
or instance will be assigned to one cluster ID.
This kind of information that was identified automatically
by the algorithm may be a useful insight
about the dataset that we can use.
Clustering is used in a wide variety
of use cases in the industry.
For example, it is used for
market or also called customer segmentations.
I mean, all business companies today would like
to better know and understand their customers, who
they are and what's driving their purchase decisions.
This kind of segmentation can help
to adapt products, services, and also
market campaigns to each identified segment.
For example, suppose a business has data
about customers such as demographic information and
also their historic purchasing behavior.
A clustering algorithm can identify subsegments of the
whole market where a particular type of product
is very successful in helping to design a
focused market a message to that specific segment.
Another interesting use case is called
anomaly detection or outlier detection.
For example, the scenario that you need to detect
defects in a manufacturing process of some product.
So there will be all kind of sensors that
are measuring different physical characteristics of the products.
And then you can run such clustering algorithms to
find data points that are too far from the
center of a specific cluster, which makes them look
like an anomaly, like the size of that product,
the boundary of that product, and so on.
Another example will be if I'm taking pictures
of a product during the manufacturing process and
then trying to identify products with defects using,
again the method of clustering.
The third one that I would like
to mention is called semi-supervised learning.
This is a method that is sitting
between supervised learning and unsupervised learning.
The idea here is that we can run
a clustering algorithm on an unlabeled dataset
that will create few clusters as labels, like
cluster number one, number two, et cetera.
Then I will get a very small amount of clusters
that I can manually label, like, this cluster is red,
this cluster is blue, this cluster is green, or whatever
criteria that I would like to use.
And then I can propagate those labels to
all the instances in the same cluster.
And now suddenly I have labeled dataset that can
be used for training a model in supervised learning.
Very interesting approach.
Let's move to the next common task
in unsupervised learning called dimension reduction.
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