An Apriori Based Algorithm to Mine Association Rules with Inter Itemset Distance

Abstract

Association rules discovered from transaction databases can be large in number. Reduction of association rules is an issue in recent times. Conventionally by varying support and confidence number of rules can be increased and decreased. By combining additional constraint with support number of frequent itemsets can be reduced and it leads to generation of less number of rules. Average inter itemset distance(IID) or Spread, which is the intervening separation of itemsets in the transactions has been used as a measure of interestingness for association rules with a view to reduce the number of association rules. In this paper by using average Inter Itemset Distance a complete algorithm based on the apriori is designed and implemented with a view to reduce the number of frequent itemsets and the association rules and also to find the distribution pattern of the association rules in terms of the number of transactions of non occurrences of the frequent itemsets. Further the apriori algorithm is also implemented and results are compared. The theoretical concepts related to inter itemset distance are also put forward.