It is clear that droughts have a fundamental impact on many different elements of society. To reduce the drought-related losses, it is necessary to give decision makers visibility into relationships of oceanic-atmospheric parameters that cause drought. The main target of this paper is to show the efficiency of data mining methods (especially association rules mining) for Identifying the associations between local droughts and large scale oceanic-atmospheric climate phenomena such as Sea Surface Temperature (SST). In this paper, association rules mining technique was offered to discover affiliation between drought of Urmia synoptic station (located in Iran) and de-trend SSTs of the Black, Mediterranean and Red Seas. To examine the accuracy of the rules, the confidence measures of the rules were calculated and compared for different considering lag times. The computed measures confirm reliable performance of the association rules mining method to monitor local drought so that the confidence between the monthly Standardized Precipitation Index (SPI) values and the de-trend SST of seas is higher than 87 percent.
