Place these two centroids at random places. In k-means clustering the objects are divided into several clusters mentioned by the number K So if we say K 2 the objects are divided into two clusters c1 and c2 as shown. Given two data points a and b we need to find a way to define a distance between them.
Assign every data point to the centroid to which it is nearest hence make two clusters.
Assign every data point to the centroid to which it is nearest hence make two clusters. Partitioning clustering is split into two subtypes - K-Means clustering and Fuzzy C-Means. Finding the centre or Mean of multiple points If you are already familiar with. Ie it results in an attractive tree-based representation of the observations called a Dendrogram.