1. Introduction 1.1 K-means algorithm: K-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean.It is one of the simplest unsupervised learning algorithms that solve the well known clustering problem.The k-means algorithm is an evolutionary algorithm that gains its name from its method of operation. The algorithm clusters observations into k groups, where k is provided as an input parameter.It then assigns each observation to clusters based upon the observationâs proximity to the mean of the cluster.t is one of the simplest unsupervised learning algorithms that solve the well known clustering problem. The k-means algorithm is an evolutionary algorithm that gains its name from its method of operation. The algorithm clusters observations into k groups, where k is provided as an input parameter.