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In this paper we have proposed an improved approach to extract rare association rules. Rare association rules are the association rules containing rare items. Rare items are less frequent items. For extracting rare itemsets, the single minimum support (minsup) based approaches like Apriori approach suffer from ldquorare item problemrdquo dilemma. At high minsup value, rare itemsets are missed, and at low minsup value, the number of frequent itemsets explodes. To extract rare itemsets, an effort has been made in the literature in which minsup of each item is fixed equal to the percentage of its support. Even though this approach improves the performance over single minsup based approaches, it still suffers from ldquorare item problemrdquo dilemma. If minsup for the item is fixed by setting the percentage value high, the rare itemsets are missed as the minsup for the rare items becomes close to their support, and if minsup for the item is fixed by setting the percentage value low, the number of frequent itemsets explodes. In this paper, we propose an improved approach in which minsup is fixed for each item based on the notion of ldquosupport differencerdquo. The proposed approach assigns appropriate minsup values for frequent as well as rare items based on their item supports and reduces both ldquorule missingrdquo and ldquorule explosionrdquo problems. Experimental results on both synthetic and real world datasets show that the proposed approach improves performance over existing approaches by minimizing the explosion of number of frequent itemsets involving frequent items and without missing the frequent itemsets involving rare items.