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

Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 399-404, 2018, DOI: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… More >

Yuhang Long¹, Wensheng Tang^{1, *}, Bo Yang^{1, *}, Xinyu Wang², Hua Ma¹, Hang Shi¹, Xueyu Cheng³

CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1445-1469, 2020, DOI:10.32604/cmc.2020.09944

Abstract Currently, the top-rank-k has been widely applied to mine frequent patterns with a rank not exceeding k. 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-k frequent patterns hybrid mining algorithm (GTK) is proposed in this paper. In this algorithm, top-rank-k patterns are stored in a static doubly linked list called RSL, and the patterns are divided into short patterns and long patterns. The short… More >