k-means is one of the simplest unsupervised learning algorithms that solve the well known clustering problem. The procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters) fixed apriori. The main idea is to define k centers, one for each cluster. These centers should be placed in a cunning way because of different location causes different result. So, the better choice is to place them as much as possible far away each other.-k-means is　one of　the simplest unsupervised　learning　algorithms　that　solve　the well　known clustering problem. The procedure follows a simple and　easy　way　to classify a given data set　through a certain number of　clusters (assume k clusters) fixed apriori. The　main　idea　is to define k centers, one for each cluster. These centers　should　be placed in a cunning　way　because of　different　location　causes different　result. So, the better　choice　is　to place them　as　much as possible　far away　each other.