Open Access

ARTICLE

An algorithm for Fast Mining Top-rank-k Frequent Patterns Based on Node-list Data Structure

Qian Wang^a,b,c, Jiadong Ren^a,b, Darryl N Davis^c, Yongqiang Cheng^c

^a College of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei, China;
^b Computer Virtual Technology and System Integration Laboratory of Hebei Province, China;
^c Department of Computer Science, University of Hull, Hull, UK

* Corresponding Author: Jiadong Ren,

Intelligent Automation & Soft Computing 2018, 24(2), 399-404. https://doi.org/10.1080/10798587.2017.1340135

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.

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Keywords

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