A GPU-based closed frequent itemsets mining algorithm over stream

Closed frequent itemsets are one of several condensed representations of frequent itemsets, which store all the information of frequent itemsets using less space, thus being more suitable for stream mining. This paper considers a problem that to the best of our knowledge has not been addressed, namely, how to use GPU to mine closed frequent itemsets in an incremental fashion. Our method employs a single-instruction-multiple-data architecture to accelerate the mining speed using a bitmap data representation of frequent itemsets. Our experimental results show that our algorithm achieves a better performance in running time.