Association Rule discovery has been an important problem of investigation in knowledge discovery and data mining. An association rule describes associations among the sets of items which occur together in transactions of databases.The Association Rule mining task consists of finding the frequent itemsets and the rules in the form of conditional implications with respect to some prespecified threshold values of support and confidence.The interestingness of Association Rules are determined by these two measures. However, other measures of interestingness like lift and conviction are also used. But, there occurs an explosive growth of discovered association rules and many of such rules are insignificant. In this paper we introduce a new measure of interestingness called Inter Itemset Distance or Spread and implemented this notion based on the approaches of the apriori algorithm with a view to reduce the number of discovered Association Rules in a meaningful manner. An analysis of the working of the new algorithm is done and the results are presented and compared with the results of conventional apriori algorithm
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