Abstrato

An Efficient Numerical Methods for the Prediction of Clusters using K-means Algorithm with Bisection method for Comparing Uniform and Random Distribution Data Points

D.Napoleon, M.Praneesh

In this paper we extract the cluster by using numerical as well as statistical methods for improving efficiency using efficient algorithms of k-means in data mining. So, Data mining is defined as finding hidden information in a database it has been called exploratory data analysis, data driven discovery, and deductive learning.[1] clustering is usually accomplished by determining the similarity among the data on predefined attributes. The most similar data are grouped into clusters. This paper proposes a method for making the k-means algorithm and Bisection method for more effective and efficient, so as to getting better cluster.