Let’s see how we formulate the learning problem in unsupervised
learning. When we have a dataset without any labels, we assume that
the data is generated because of latent variables that govern the
distribution in some way. The process of learning can then proceed in
a hierarchical manner, starting from the individual data points. We
can build deeper levels of representation for the data.
Clustering data with K‑Means algorithm
Clustering is one of the most popular unsupervised learning
techniques. This technique is used to analyze data and find clusters
within that data. In order to find these clusters, we use some kind of
similarity measure such as Euclidean distance, to find the subgroups.
This similarity measure can estimate the tightness of a cluster. We
can say that clustering is the process of organizing our data into
subgroups whose elements are similar to each other.
Our goal is to identify the intrinsic properties of data points that
make them belong to the same subgroup. There is no universal
similarity metric that works for all the cases. It depends on the
problem at hand. For example, we might be interested in finding the
representative data point for each subgroup or we might be interested
in finding the outliers in our data. Depending on the situation, we will
end up choosing the appropriate metric.
K-Means algorithm is a well-known algorithm for clustering data. In
order to use this algorithm, we need to assume that the number of
clusters is known beforehand. We then segment data into K
subgroups using various data attributes. We start by fixing the
number of clusters and classify our data based on that. The central
idea here is that we need to update the locations of these K centroids
with each iteration. We continue iterating until we have placed the
centroids at their optimal locations.
We can see that the initial placement of centroids plays an important
role in the algorithm. These centroids should be placed in a clever
manner because this directly impacts the results. A good strategy is to
place them as far away from each other as possible. The basic K-
Means algorithm places these centroids randomly where K-Means++
chooses these points algorithmically from the input list of data
points. It tries to place the initial centroids far from each other so that
