Cluster analysis is a technique used to group similar objects into clusters based on their features or attributes. The main objective of cluster analysis is to identify structure in the data and group similar objects together. This technique is widely used in various fields, including market segmentation, image analysis, customer profiling, and many others. In this blog, we will discuss the basics of cluster analysis, its types, distance measures, and applications.

Cluster analysis, also known as clustering, is a technique used to divide a large dataset into smaller groups, or clusters, based on their similarity. The similarity between objects is measured using various distance measures, such as Euclidean distance, Manhattan distance, or cosine similarity. The main goal of cluster analysis is to find structure in the data, identify patterns, and group similar objects together.