LAUSR

Abstract Most of the mines operating in Iran are classified into Artisanal and Small-scale mines (ASM). ASM accounts for 98.3% of the country's 10,000 mines, more than 80% of employment, and about 65% of the mining sector production. However, these mines face liquidity, legal and administrative issues, sales market, infrastructure, and investment. Though, their activation and restoration require many limited resources compared to large mines. Therefore, it is undeniable to use this sector's capacity to create sustainable employment and development in deprived areas of the country (due to ASM's geographical extent) and help supply raw materials. Hence, in this paper, in the first step, identifying and troubleshooting in these mines was done based on field information and organ documents such as Ministry of Industry, Mine and Trade, Iranian Mines and Mining Industries Development and Renovation Organization (IMIDRO), Iran Minerals Procurement and Production Company, etc. A database consisting of 313 mines from 29 provinces of the country was formed and evaluated using a data mining approach. In this study, two data mining methods, including clustering and decision tree, were used. As a result, appropriate divisions were presented based on available information without any previous hypotheses or backgrounds. The purpose of these divisions was to provide an appropriate classification of mines by applying different estimators to make strategic decisions. Because at present, in most decisions, mines are divided solely based on an estimator such as geographical distance, mineral genus, annual production.