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...