Frequent itemset mining (FIM) is one of the most important data mining tasks. The characteristics of datasets have a significant impact on the performance of FIM algorithms. In the era of big data, sparseness, a typical feature of big data, brings severe challenges to the performance of traditional FIM algorithms. Aiming at the problem of how to perform FIM in sparse datasets efficiently, based on the characteristics of sparse datasets, we analyze the main effects of sparse datasets on the performance of three FIM algorithms, summarize current sparse datasets FIM algorithms, discuss the optimization strategies used in these algorithms, and analyse the performance of the typical sparse datasets FIM algorithms through experiments. Experimental results show that the pattern growth algorithm with pseudo-structural strategy is most suitable for FIM in sparse datasets and outperforms the other two algorithms in both operation time and storage space.