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It is a difficult task to set rare association rules to handle unpredictable items since approaches such as apriori algorithm and frequent pattern-growth, a single minimum support application based suffers from low or high minimum support. If minimum support is set high to cover the rarely appearing items it will miss the frequent patterns involving rare items since rare items fail to satisfy high minimum support. In the literature, an effort has been made to extract rare association rules with multiple minimum supports. In this paper, we explore the probability and propose multiple minsup based apriori-like approach called Probability Apriori Multiple Minimum Support (PAMMS) to efficiently discover rare association rules. Experimental results show that the proposed approach is efficient.