Information Technology Journal1812-56381812-5646Asian Network for Scientific Information10.3923/itj.2006.551.559DehuriSatchidanandan MohapatraChinmay GhoshAshish MallRajib 3200653Data clustering is an unsupervised task that can generate different shapes of clusters for a particular type of data set. Hence choosing an algorithm for a particular type of data set is a difficult problem. This study presents the choice of an appropriate clustering algorithm by a comparative study of three representative techniques like K-means, Kohonen`s Self Organizing Map (SOM) and Density Based Spatial Clustering of Applications with Noise (DBSCAN) based on the extensive simulation studies. Comparison is performed on the basis of cluster quality index `ß`, percentage of samples correctly classified and CPU time. The experimental results show that if the clusters are of arbitrary shape, a density based clustering algorithm like DBSCAN is preferable, where as if the clusters are of hyper spherical or convex shape and well-separated then the SOM or K-means is preferable.]]>Ben-Dor, A. and Z. Yakhini,1999Blake, C.L. and C.J. Merz,1998Cadez, I.V., P. Smyth and H. Mannila,2001Cutting, D.R., D.R. Karger, J.O. Pedersen and J.W. Tukey,1992Dhillon, I., J. Fan and Y. Guan,2001Duda, R. and P. Hart,1973Ester, M., H.P. Kriegel, J. Sander and X. Xu,1996Ester, M., A. Frommelt, H.P. Kreigel and J. Sander,2000Fayyad, U.M., G. Piatetsky-Shapiro and P. Smyth,1996Forgey, E.,1965Foss, A., W. Wang and O. Zaane,2001Hartigan, J.A.,1975Hartigan, J.A. and M.A. Wong,1979K-means clustering algorithm.]]>Heer, I. and E. Chi,2001Jain, A.K., M.N. Murty and P.J. Flynn,1999Kohonen, T.,1990Steinbach, M., G. Karypis and V. Kumar,2000Xu, X., M. Ester, H.P. Kriegel and J. Sander,1998Sander, J., M. Ester, H.P. Kriegel and X. Xu,1998