CUSE: A novel cube-based approach for sequential pattern mining
Mohammad Karim Sohrabi, Vahid Ghods · 2016
Sequences are one of the most important types of patterns which are extracted from datasets and are used to construct association rules. Several sequential pattern mining methods have been proposed in the literature. This paper introduces a novel bit wise approach to compress and represent the sequence database as a 3-dimentional array and use a corresponding mining method to extract frequent sequences from the compressed structure. Experimental results and performance study show that this algorithm outperforms the best previously developed algorithms.