@Article{cmc.2020.09944,
AUTHOR = {Yuhang Long, Wensheng Tang, Bo Yang, Xinyu Wang, Hua Ma, Hang Shi, Xueyu Cheng},
TITLE = {GTK: A Hybrid-Search Algorithm of Top-Rank-<i>k</i> Frequent  Patterns Based on Greedy Strategy},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {63},
YEAR = {2020},
NUMBER = {3},
PAGES = {1445--1469},
URL = {http://www.techscience.com/cmc/v63n3/38886},
ISSN = {1546-2226},
ABSTRACT = {Currently, the top-rank-<i>k</i> has been widely applied to mine frequent patterns 
with a rank not exceeding <i>k</i>. In the existing algorithms, although a level-wise-search 
could fully mine the target patterns, it usually leads to the delay of high rank patterns 
generation, resulting in the slow growth of the support threshold and the mining 
efficiency. Aiming at this problem, a greedy-strategy-based top-rank-<i>k</i> frequent patterns 
hybrid mining algorithm (GTK) is proposed in this paper. In this algorithm, top-rank-<i>k</i>
patterns are stored in a static doubly linked list called RSL, and the patterns are divided 
into short patterns and long patterns. The short patterns generated by a rank-first-search 
always joins the two patterns of the highest rank in RSL that have not yet been joined. On 
the basis of the short patterns satisfying specific conditions, the long patterns are 
extracted through level-wise-search. To reduce redundancy, GTK improves the 
generation method of subsume index and designs the new pruning strategies of 
candidates. This algorithm also takes the use of reasonable pruning strategies to reduce 
the amount of computation to improve the computational speed. Real datasets and 
synthetic datasets are adopted in experiments to evaluate the proposed algorithm. The 
experimental results show the obvious advantages in both time efficiency and space 
efficiency of GTK.},
DOI = {10.32604/cmc.2020.09944}
}