In this paper I proposed modified K-means algorithm as the means to assess scientific authors performance by using their h,g-indices values. K-means suffers from poor computational scaling and efficiency as the number of clusters has to be supplied by the user. In this work, I introduce a modification of K-means algorithm that efficiently searches the data to cluster points by compute the sum of squares within each cluster which makes the program to select the most promising subset of classes for clustering. The proposed algorithm was tested on IRIS and ZOO data sets as well as on our local dataset comprising of h- and g-indices, which are the prominent markers for scientific excellence of authors publishing papers in various national and international journals. Results from analyses reveal that the modified k-means algorithm is much faster and outperforms the conventional algorithm in terms of clustering performance, measured by the data discrepancy factor.