@Article{10798587.2017.1340135,
AUTHOR = {Qian Wang, Jiadong Ren, Darryl N Davis, Yongqiang Cheng},
TITLE = {An algorithm for Fast Mining Top-rank-k Frequent Patterns Based on Node-list Data Structure},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {24},
YEAR = {2018},
NUMBER = {2},
PAGES = {399--404},
URL = {http://www.techscience.com/iasc/v24n2/39767},
ISSN = {2326-005X},
ABSTRACT = {Frequent pattern mining usually requires much run time and memory usage. In some applications, only 
the patterns with top frequency rank are needed. Because of the limited pattern numbers, quality of 
the results is even more important than time and memory consumption. A Frequent Pattern algorithm 
for mining Top-rank-K patterns, FP_TopK, is proposed. It is based on a Node-list data structure extracted 
from FTPP-tree. Each node is with one or more triple sets, which contain supports, preorder and postorder transversal orders for candidate pattern generation and top-rank-k frequent pattern mining. FP_
TopK uses the minimal support threshold for pruning strategy to guarantee that each pattern in the 
top-rank-k table is really frequent and this further improves the efficiency. Experiments are conducted 
to compare FP_TopK with iNTK and BTK on four datasets. The results show that FP_TopK achieves 
better performance.},
DOI = {10.1080/10798587.2017.1340135}
}