  The number of accidents and health diseases which are increasing at an
alarming rate are resulting in a huge increase in the demand for blood. There
is a necessity for the organized analysis of the blood donor database or blood
banks repositories. Clustering analysis is one of the data mining applications
and K-means clustering algorithm is the fundamental algorithm for modern
clustering techniques. K-means clustering algorithm is traditional approach and
iterative algorithm. At every iteration, it attempts to find the distance from
the centroid of each cluster to each and every data point. This paper gives the
improvement to the original k-means algorithm by improving the initial
centroids with distribution of data. Results and discussions show that improved
K-means algorithm produces accurate clusters in less computation time to find
the donors information.
