Introduction

Unlike a classification problem, where we know a class for each observation (often referred to as supervised training or training with a teacher), clustering models find patterns in data without requiring labels (called unsupervised learning paradigm).

The clustering methods put a set of unknown observations into buckets based on how similar the observations are. Such analysis aids the exploratory phase when an analyst wants to see if there are any patterns occurring naturally in the data.