Incrementally updating the discovered sequential patterns based on pre-large concept

Jerry Chun Wei Lin, Tzung Pei Hong, Wensheng Gan, Hsin Yi Chen, Sheng Tun Li

研究成果: Article同行評審

16 引文 斯高帕斯(Scopus)

摘要

Mining useful information from large databases has become an important research area in recent years. Among the classes of knowledge derived, sequential pattern can be applied in many domains, such as market analysis, web click streams, and biological data. The fast updated sequential pattern tree (FUSP-tree) algorithm was proposed to update discovered sequential patterns in incremental mining. However, it must rescan the original database for maintaining discovered sequential patterns. This study proposes the PreFUSP-TREE-INS algorithm based on the pre-large concept for maintaining discovered sequential patterns without rescanning the original database until the cumulative number of newly added customer sequences exceeds a safety bound. The execution time for reconstructing the tree when old or new customer sequences are added into the original database is reduced by using pre-large sequences. The pre-large sequences are defined by lower and upper support thresholds that prevent the movement of sequences directly from large to small and vice versa. Experiments are conducted to show the performance of the proposed algorithm for various minimum support thresholds and ratios of inserted sequences.

原文English
頁(從 - 到)1071-1089
頁數19
期刊Intelligent Data Analysis
19
發行號5
DOIs
出版狀態Published - 2015 9月 8

All Science Journal Classification (ASJC) codes

  • 理論電腦科學
  • 電腦視覺和模式識別
  • 人工智慧

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