Mining Frequent Closed Itemsets in Large High Dimensional Data

Large high-dimensional data have posed great challenges to existing algorithms for frequent itemsets mining. To solve the problem, a hybrid method, consisting of a novel row enumeration algorithm and a column enumeration algorithm, is proposed. The intention of the hybrid method is to decompose the mining task into two subtasks and then choose appropriate algorithms to solve them respectively. The novel algorithm, i.e., Intertransaction is based on the characteristic that there are few common items between or among long transactions. In addition, an optimization technique is adopted to improve the performance of the intersection of bit-vectors. Experiments on synthetic data show that our method achieves high performance in large high-dimensional data.