K means clustering

.It is the mostly used clustering methods. This algorithm assumes a geometric interpretation of the data sets and the datasets can be represented as some points in an n-dimensional data space. It aims to partition the n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. The steps for K-means of clustering are as follows:

1. Construct a partition of a database D of n different objects into a set of k cluster centre. Here, we select the k clusters randomly.

2. Now, assign every item of the datasets to its nearest cluster centroid.

3. Recalculate the centre of the clusters and move items nearby to the clusters.

4. Repeat steps 2 and 3 until the clusters stops changing their positions and move cluster centre to cluster