Skip to main content

Research Repository

Advanced Search

An algorithm for fast mining top-rank-k frequent patterns based on node-list data structure

Wang, Qian; Ren, Jiadong; N Davis, Darryl; Cheng, Yongqiang


Qian Wang

Jiadong Ren

Darryl N Davis


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 post-order 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.


Wang, Q., Ren, J., N Davis, D., & Cheng, Y. (2018). An algorithm for fast mining top-rank-k frequent patterns based on node-list data structure. Intelligent Automation and Soft Computing, 24(2), 399-404.

Acceptance Date May 20, 2017
Online Publication Date Sep 15, 2017
Publication Date 2018
Deposit Date Jul 11, 2017
Publicly Available Date Oct 27, 2022
Journal Intelligent automation & soft computing
Print ISSN 1079-8587
Electronic ISSN 2326-005X
Publisher Taylor and Francis
Peer Reviewed Peer Reviewed
Volume 24
Issue 2
Pages 399-404
Keywords Data mining, Frequent pattern, Top-rank-k frequent pattern, FTPP-tree, Node-list
Public URL
Publisher URL
Additional Information Peer Review Statement: The publishing and review policy for this title is described in its Aims & Scope.; Aim & Scope:


You might also like

Downloadable Citations