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,19991999pp: 1114Blake, C.L. and C.J. Merz,19981st Edn.,Cadez, I.V., P. Smyth and H. Mannila,20012001pp: 3746Cutting, D.R., D.R. Karger, J.O. Pedersen and J.W. Tukey,19921992pp: 318329Dhillon, I., J. Fan and Y. Guan,20012001Duda, R. and P. Hart,1973Ester, M., H.P. Kriegel, J. Sander and X. Xu,19961996pp: 226231Ester, M., A. Frommelt, H.P. Kreigel and J. Sander,20004193216Fayyad, U.M., G. Piatetsky-Shapiro and P. Smyth,19961996pp: 1-34pp: 1-34Forgey, E.,196521768780Foss, A., W. Wang and O. Zaane,20012001pp: 4150Hartigan, J.A.,19754th Edn.,Hartigan, J.A. and M.A. Wong,1979K-means clustering algorithm.]]>28100108Heer, I. and E. Chi,20012001pp: 5158Jain, A.K., M.N. Murty and P.J. Flynn,199931264323Kohonen, T.,19907814641480Steinbach, M., G. Karypis and V. Kumar,20002000pp: 12Xu, X., M. Ester, H.P. Kriegel and J. Sander,19981998pp: 324331Sander, J., M. Ester, H.P. Kriegel and X. Xu,19982169194