The Janja region is located 30 kilometres NW of Sefidabeh city in Sistan and Baluchistan province, Iran. Considering the evidence of gold mineralization, it is necessary to identify the promising mineral areas in this region. Clustering is an important method in data mining science, and it is used to analyse and examine a significant amount of data with various characteristics. The purpose of this research is to investigate the ability of clustering methods to determine gold anomaly areas in the studied area and to compare the performances of each method. Hence, the K-Means and K-Medoids clustering algorithms have been used to investigate the behaviour of gold and copper elements towards each other. For this purpose, first, the number of clusters was changed from k = 3 to k = 11 and then the optimal number of clusters was obtained using the Davies–Bouldin, Silhouette and Calinski–Harabasz criteria. According to the results obtained, it can be said that both methods for estimating the gold grade are suitable and effective methods due to the high correlation coefficient. Also, the widely used K-Nearest Neighbour (KNN) method was used under the grade values of gold, silver, copper, lead and zinc in order to simulate and determine promising areas of gold mineralization. The results show that the KNN method has been effective for finding gold anomalies with proper performance.

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