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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

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 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.

Journal Article Type Article
Publication Date 2018
Journal Intelligent automation & soft computing
Print ISSN 1079-8587
Electronic ISSN 2326-005X
Publisher Taylor & Francis
Peer Reviewed Peer Reviewed
Volume 24
Issue 2
Pages 399-404
DOI https://doi.org/10.1080/10798587.2017.1340135
Keywords Data mining, Frequent pattern, Top-rank-k frequent pattern, FTPP-tree, Node-list
Publisher URL http://www.tandfonline.com/doi/abs/10.1080/10798587.2017.1340135?needAccess=true&journalCode=tasj20
Additional Information Peer Review Statement: The publishing and review policy for this title is described in its Aims & Scope.; Aim & Scope: http://www.tandfonline....cope&journalCode=tasj20

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