Journal of Artificial Intelligence1994-54502077-2173Asian Network for Scientific Information10.3923/jai.2013.257.265SemanAliAbu BakarZainabMohd. SapawiAzizianOthmanIda Rosmini4201364Medoid-based method is an alternative technique to centroid-based method for
partitional clustering algorithms. This method has been incorporated in a recently
introduced clustering algorithm for categorical data, called k-Approximate Modal
Haplotype (k-AMH) algorithm. This study reports the performance evaluation between
the medoid-based method represented by the k-AMH algorithm and the centroid-based
method represented by the extended k-Mode algorithm, the k-Population algorithm
and the new Fuzzy k-Mode algorithm in clustering common categorical data. Nine
common categorical data sets were used in the experiments to compare the performance
of both methods using clustering accuracy scores. In overall results, the medoid-based
method of k-AMH algorithm produced significant results for all data sets. The
method showed its advantage of obtaining the highest clustering accuracy of
0.94 when clustering large number of clusters. This result indicated that the
medoid-based method has a significant contribution for clustering categorical
data, particularly for clustering large number of clusters.]]>Dunham, M.,2003Han, J. and M. Kamber,2001Han, J., M. Kamber and A.K.H. Tung,2001He, Z., X. Xu and S. Deng,2007Huang, Z. and M.K. Ng,1999Huang, Z.,1998Kaufman, J. and P.J. Rousseeuw,1987Kaufman, L. and P.J. Rousseeuw,1990Kim, D.W., K.Y. Lee, D. Lee and K.H. Lee,2005Li, M.J., M.K. Ng, Y.M. Cheung and J.Z. Huang,2008MacQueen, J.,1967Ng, R. and J. Han,1994Ng, M.K. and L. Jing,2009Ng, M.K., M.J. Li, J.Z. Huang and Z. He,2007Ralambondrainy, H.,1995Seman, A., Z.A. Bakar and M.N. Isa,2012Tan, P.N., M. Steibach and V. Kumar,2006