An Improvement in K-mean Clustering Algorithm Using Better Time and Accuracy

Abstract

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters).K-means is one of the simplest unsupervised learning algorithms that solve the well known clustering problem. The process of k means algorithm data is partitioned into K clusters and the data are randomly choose to the clusters resulting in clusters that have the same number of data set. This paper is proposed a new K means clustering algorithm we calculate the initial centroids systemically instead of random assigned due to which accuracy and time improved.