A Survey on Clustering Principles with K-means Clustering Algorithm Using Different Methods in Detail?

Clustering is an essential task in Data Mining process which is used for the purpose to make groups or clusters of the given data set based on the similarity between them. K-Means clustering is a clustering method in which the given data set is divided into K number of clusters. This paper is intended to give the introduction about K-means clustering and its algorithm. The experimental results of K-means clustering and its performance in case of execution time are discussed here. But there are certain limitations in K-means clustering algorithm such as it takes more time for execution. So in order to reduce the execution, time we are using the Ranking Method and Query Redirection. And also shown that how clustering is performed in less execution time as compared to the traditional method. This work makes an attempt at studying the feasibility of K-means clustering algorithm in data mining using different methods.