Abstract: Finding of hidden and previously unknown information in large collection of data is the process of data mining. Mining association rules is a very important model in data mining. Using association rules different type of regularities and patterns can be identified. In most of the previous approaches a single minimum support threshold value is used for all the items or itemsets. But all the items in an itemset do not behave in the same way. Some appear very frequently and some very rarely. Therefore the support requirements should vary with different items. In this paper, a simple algorithm based on the Apriori approach is proposed to find the large-itemsets and association rules under arithmetic mean constraint and with multiple minimum supports to overcome the above mentioned problem. The proposed algorithm is easy and efficient and it saves time by focusing only on necessary associations.