A new approach for improving clustering algorithms performance

Clustering represents one of the most popular and used Data Mining techniques due to its usefulness and the wide variations of the applications in real world. Defining the number of the clusters required is an application oriented context, this means that the number of clusters k is an input to the whole clustering process. The proposed approach represents a solution for estimating the optimum number of clusters. It is based on the use of iterative K-means clustering under three different criteria; centroids convergence, total distance between the objects and the cluster centroid and the number of migrated objects which can be used effectively to ensure better clustering accuracy and performance. A total of 20000 records available on the internet were used in the proposed approach to test the approach. The results obtained from the approach showed good improvement on clustering accuracy and algorithm performance over the other techniques where centroids convergence represents a major clustering criteria. C# and Microsoft Excel were the software used in the approach.