Abstract

Emergence of modern techniques for scientific data collection has resulted in large scale accumulation of data pertaining to diverse fields. Conventional database querying methods are inadequate to extract useful information from huge data analysis. Cluster analysis is one of the major data analysis methods and k-means clustering algorithm Emergence of modern techniques for scientific data collection has resulted in large scale accumulation of data pertainting diverse felids. Conventional Data base methods are inadequate to extract useful information from huge data banks. Cluster analysis is one of the major data analysis methods and the k-means clustering algorithm is widely used for many practical applications. But the original k-means algorithm is computationally expensive and the quality of the resulting clusters heavily depends on the selection of initial cancroids. Several methods have been proposed in the literature for improving the performance of the k-means clustering algorithm. The k-means algorithm is computationally expensive and requires time proportional to the product of the number of data items, number of clusters and the number of iterations.This papert proposes a method for making the algorithm more effective and efficient.