A High Performance Modified K-Means Algorithm for Dynamic Data Clustering in Multi-core CPUs Based Environments

K-means algorithm is one of the most widely used methods in data mining and statistical data analysis to partition several objects in K distinct groups, called clusters, on the basis of their similarities. The main problem of this algorithm is that it requires the number of clusters as an input data, but in the real life it is very difficult to fix in advance such value. In this work we propose a parallel modified K-means algorithm where the number of clusters is increased at run time in a iterative procedure until a given cluster quality metric is satisfied. To improve the performance of the procedure, at each iteration two new clusters are created, splitting only the cluster with the worst value of the quality metric. Furthermore, experiments in a multi-core CPUs based environment are presented.