A well-known algorithm of clustering is k-means by which the data are divided into k classes based upon a distance criterion. In present research, applying k-means method for classifying data derived from exploration boreholes in Parkam deposit, the optimum k has been calculated and then the data have been clustered and the relative geochemical behavioral characteristics analyzed. The criterion used for determining the optimum k ranged the number of classes from k=3 to k=10 and afterwards, analyzed derived classifications in order to choose the optimum k. Results showed that class numbers of k=3 in case of Cu and Mo, k=4 in case of Cu and Pb and k=3 in case of Cu and Zn were optimized class numbers. After clustering, increasing Cu grade values resulted in significant increase in Mo grades, a significant decrease in Pb grades (down to 0.16) followed by an increase and Zn grades varying as in the case of Pb. With regards to the relationships between these elements it can be concluded that the meteoric waters trigger the mobilization of Pb and Zn from the potassic zone to the phyllic but the influence of meteoric waters is not to the degree that it could cause the mobilization of Cu and this element together with Mo remain immobile and this makes the relationship between Cu and Mo linear and it is probably due to this matter which makes the Parkam porphyry system uneconomical.