Research on the Distributed Treatment of Frequent Itemsets Extraction Based on Pruned Concept Lattices

The effective representation and amalgamating extraction method of frequent itemsets in distributed databases is important to improve the result of distributed association rules mining. The common methods are inefficient due either to higher number of database scan or to larger amount of candidate itemsets for communication. Based on discussing the relation between the concept of pruned concept lattice (PCL) and the representation of frequent itemsets, the closed frequent itemsets of PCL is defined. UMPCL_I, an approximate amalgamation and extraction method of frequent itemsets in horizontally partitioned databases based on multiple PCL, is proposed. The main ideas of this method are using a frequent concept to represent some few of frequent itemsets, and using a local support slightly lower than global support to prune sub-lattices before been amalgamated to decrease the size of exchanged messages. The theoretic analysis and experiment show that such method is efficient