New exploration methods in mining industry is an important issue due to that fact that surficial deposits are diminishing and the rate of exploration of blind deposits are less than the expected global demand. Exploration is a high risk and expensive acivity so that it may cause enormous disadvantages if it is not conducted in proper direction. Drilling is the main costly part of exploration and identification of borehole locations is a critical decision. Using surficial data such as soil/rock geochemical analyses for choosing the drilling locations is very risky; therefore, intelligent methods can be used these days as alternatives. Copper is a high demanding metal, which is used in several industries such as electric, electronic, military, ans so on. The most common type of copper deposits are porphyry deposits that produce the major copper in the world. Sungun porphyry copper deposit after Sarcheshmeh is the second largets porphyry Cu mine in Iran. It is located in NW of Iran within the Urumiyeh-Dokhtar Magmatic Arc. In this thesis, different unsupervised clusteringsmethods have been applied on the soil geochemical data of the Songun deposit. Then, seven types of clusterings methods such as K-mean and FCM as
