A Survey on Various K-Means algorithms for Clustering

Data Mining is the process to find out the data from large data sets and transform into the valuable information. In this paper we are presenting about the clustering techniques and the impact noise on clustering techniques. Clustering is important in data analysis and data mining applications. It is the task of grouping a set of objects so that objects in the same group are more similar to each other than to those in other groups (clusters). Clustering can be done by the different no. of algorithms such as hierarchical, partitioning, grid and density based algorithms. Hierarchical clustering is the connectivity based clustering. Partitioning is the centroid based clustering. K-Mean is widely used clustering algorithm.