Comprehensive mining of frequent itemsets for a combination of certain and uncertain databases

The mechanism of Frequent Itemset Mining can be performed by using sequential algorithms like Apriori on a standalone system, or it can be applied using parallel algorithms like Count Distribution on a distributed system. Due to communication overhead in parallel algorithms and exponential candidate generation, many algorithms were developed for calculating frequent items either over the certain or uncertain database. Yet not a single algorithm is developed so far which can cover the requirement of generating frequent itemset by combining both the databases. We had proposed earlier MasterApriori algorithm which is used to calculate Approximate Frequent Items for a combination of certain and uncertain databases with the support of Apriori for Certain and Expected support based UApriori for the uncertain database. In this paper, the researcher would like to extend the former work by using Poisson and Normal Distribution based UApriori for the uncertain database. In proposed algorithms, there is only one-time communication between sites where data is distributed, which reduce the communication overhead. Scalability and efficiency of proposed algorithms are then checked by using standard, and synthetic databases. The performances were then measured by comparing time taken and a number of frequent items generated by each algorithm. © 2019, Bharati Vidyapeeth's Institute of Computer Applications and Management.