Comparison of a Time Efficient Modified K-mean Algorithm with K-Mean and K-Medoid Algorithm

Clustering analysis is a descriptive task that seeks to identify homogeneous groups of objects based on the values of their attributes. This paper proposes a new algorithm for Modified K-Means clustering which executes like the K-means algorithm and k-medoids algorithms and tests several methods for selecting initial cluster. Modified K-Mean Algorithm is better in terms of number of clusters and execution time comparisons with K-Mean and K-Mediod. Proposed algorithm is evaluated using real data and results are compared with k-Means and k-medoids where it takes reduced time in computation and better performance compared to K-Means and K-Medoids algorithms.