An efficient method for mining frequent itemsets with double constraints

Constraint-based frequent itemset mining is necessary when the needs and interests of users are the top priority. In this task, two opposite types of constraint are studied, namely anti-monotone and monotone constraints. Previous approaches have mainly mined frequent itemsets that satisfy one of these two types of constraint. Mining frequent itemsets that satisfy both types is of interest. The present study considers the problem of mining frequent itemsets with the following two conditions: they include a set C0 (monotone) and contain no items of set C′1 (anti-monotone), where the intersection of C0 and C′1 is empty and they are changed regularly. A unique representation of frequent itemsets restricted on C0 and C′1 using closed itemsets and their generators is proposed. Then, an algorithm called MFS_DoubleCons is developed to quickly and distinctly generate all frequent itemsets that satisfy the constraints from the lattice of closed itemsets and generators instead of mining them directly from the database. The theoretical results are proven to be reliable. Extensive experiments on a broad range of synthetic and real databases that compare MFS_DoubleCons to dEclat-DC (a modified version of dEclat utilized to mine frequent itemsets with constraints) show the effectiveness of our approach.