Design, Analysis and Implementation of Modified K-Mean Algorithm for Large Data-Set to Increase Scalability and Efficiency

Clustering is an unsupervised learning technique. The main advantage of clustering analysis is a descriptive task that seeks to identify homogeneous groups of objects based on the values of their attributes. Clustering algorithms can be applied in many domains. we proposed an efficient, modified K-mean clustering algorithm to cluster large data-sets whose objective is to find out the cluster centers which are very close to the final solution for each iterative steps. Clustering is often done as a prelude to some other form of data mining or modeling. Performance of iterative clustering algorithms depends highly on the choice of cluster centers in each step. This algorithm is based on the optimization formulation of the problem and a novel iterative method. The cluster centers computed using this methodology are found to be very close to the desired cluster centers. The experimental results using the proposed algorithm with a group of randomly constructed data sets are very promising. The best algorithm in each category was found out based on their performance.