Frequent Itemset Mining for a Combination of Certain and Uncertain Databases

Abstract

Modern industries and business firms are widely using data mining applications in which the problem of Frequent Itemset Mining (FIM) has a major role. FIM problem can be solved by standard traditional algorithms like Apriori in certain transactional database and can also be solved by different exact (UApriori, UFP Growth) and approximate (Poisson Distribution based UApriori, Normal Distribution based UApriori) probabilistic frequent itemset mining algorithm in uncertain transactional database (database in which each item has its existential probability). In our algorithm it is considered that database is distributed among different locations of globe in which one location has certain transactional database, we call this location as main site and all other locations have uncertain transactional databases, we call these locations as remote sites. To the best of our knowledge no algorithm is developed yet which can calculate frequent itemsets on the combination of certain and uncertain transactional database. We introduced a novel approach for finding itemsets which are globally frequent among the combination of all uncertain transactional databases on remote site with certain database at main site.