Birds of a feather flock together—clustering is all about this. It's an unsupervised learning where the class or label is not known. So, you get a dataset and then with the algorithm, you divide and group the instances into different clusters with an objective of keeping all the similar ones together.
Clustering has many different application areas, such as customer segmentation, social network analysis, computational biology, and many more.
In this chapter, you will start with understanding the K-means clustering algorithm and then, you will learn how to build a model using this in ML Studio.