A simple density with distance based initial seed selection technique for K means algorithm

Open issues with respect to K means algorithm are - identifying the number of clusters, initial seed concept selection, clustering tendency, handling empty clusters, identifying outliers etc. In this paper we propose a novel and simple technique considering both density and distance of the concepts in a dataset to identify initial seed concepts for clustering. Many authors have proposed different techniques to identify initial seed concepts; but our method ensures that the initial seed concepts are chosen from different clusters which are to be generated by the clustering solution. The hallmark of our algorithm is that it is a single pass algorithm which does not require any extra parameters to be estimated. Further, our seed concepts are among the actual concepts and not the mean of representative concepts as is the case in many other algorithms. We have implemented our proposed algorithm and compared the results with the interval based technique of Khan. We see that our method outperforms the interval based method. We have also compared our method with the original random K means and K Means ++ algorithms.