Fine grained analysis of K- mean clustering and where we are using it

K-means is a centroid based algorithm that means points are grouped in a cluster according to the distance(mostly Euclidean) from centroid. Centroid-based Clustering Centroid-based clustering organizes the data into non-hierarchical clusters, in contrast to hierarchical clustering defined below. k-means is the most widely-used centroid-based clustering algorithm. Centroid-based algorithms are efficient but sensitive to initial conditions and…

In Depth Clustering Analysis

Clustering is the Unsupervised version of classification if we have labeled data then we will get classification when we grouped same labeled data . And if we don’t have the labels we will use features of the vectors to identify the same data points and group them with same properties these is what clustering is…