  Mining frequent itemsets is an essential problem in data mining and plays an
important role in many data mining applications. In recent years, some itemset
representations based on node sets have been proposed, which have shown to be
very efficient for mining frequent itemsets. In this paper, we propose
DiffNodeset, a novel and more efficient itemset representation, for mining
frequent itemsets. Based on the DiffNodeset structure, we present an efficient
algorithm, named dFIN, to mining frequent itemsets. To achieve high efficiency,
dFIN finds frequent itemsets using a set-enumeration tree with a hybrid search
strategy and directly enumerates frequent itemsets without candidate generation
under some case. For evaluating the performance of dFIN, we have conduct
extensive experiments to compare it against with existing leading algorithms on
a variety of real and synthetic datasets. The experimental results show that
dFIN is significantly faster than these leading algorithms.

Data Structures and Algorithms;Databases