Enhancing the mining top-rank-k frequent patterns

Frequent pattern mining generates a lot of candidates which spends a lot of usage memory and mining time. Besides, in real applications, a small number of frequent patterns are used. Therefore, the problem of mining top-rank-k frequent patterns (TRFPs) is an interesting topic in recent years. This paper proposes iNTK algorithm for mining TRFPs. This algorithm employs N-list structure generated by PPC-tree to reduce the memory usage. Besides, the subsume concept is also used to enhance the process of mining TRFPs. The experimental results show that iNTK outperforms NTK in terms of mining time and memory usage.