Research on Comparing the Sequential Learning with Batch Learning for K-Means

Clustering, in data mining, is useful for discovering groups and identifying interesting distributions underlying in the data. Classical K-Means algorithm can give a good result when given the cluster number. It uses batch mode to adjust the centers of clusters at the end of each epoch. Sequential mode is another method which updates the centers when each record is scanned. In this paper a K-Means algorithm employing sequential mode is proposed, implemented and compared with algorithm employing batch mode.