International Journal of Applied Information Systems (IJAIS)
Most of the clustering algorithms are based on euclidean distance as measure of similarity between data objects. Theses algorithms also require initial setting of parameters as a prior, for example the number of clusters. The euclidean distance is very sensitive to scales of variables involved and independent of correlated variables. To conquer these drawbacks a hybrid clustering algorithm based on mahalanobis distance is proposed in this paper. The reason for the hybridization is to relieve the user from setting the parameters in advance.