Unique Constraint Frequent Item Set Mining

Itemset mining identifies group of frequent itemsets that signify possibly of relevant information. Unique constraints are usually forced to emphasis the analysis on most interestingness itemsets. In this paper we proposed unique constraint based mining on relational dataset. The constrained-based mining helps us to merge all itemsets, which are interrelated to each other. Specifically it chooses itemsets with same consequent part of an association rule and evaluates the highest itemsets with minimum coverage in that relational database. This paper mainly concentrates to propose a new Apriori-based algorithm, which satisfy the certain properties of constrained itemset based mining like anti-monotonicity.