Mining frequent and top-K High Utility Time Interval-based Events with Duration patterns

Jen Wei Huang, Bijay Prasad Jaysawal, Kuan Ying Chen, Yong Bin Wu

研究成果: Article同行評審

24 引文 斯高帕斯(Scopus)

摘要

Traditional frequent sequential pattern mining only considers the time point-based item or event in the patterns. However, in many application, the events may span over multiple time points and the relations among events are also important. Time interval-based pattern mining is proposed to mine the interesting patterns of events that span over some time periods and also by considering the relations among events. Previous works of time interval-based pattern mining focused on the relations between events without considering the duration of each event. However, the same event with different time duration may cause different results. In this work, we propose two algorithms, SARA and SARS, for mining frequent time interval-based events with duration, TIED, patterns. TIED patterns not only keep the relations between two events but also reveal the time periods when each event happens and ends. For the performance evaluation, we propose a naive algorithm and modify a previous algorithm along with the implementation of SARA and SARS. The experimental results show that SARA and SARS are more efficient in terms of execution time and memory usage than other two algorithms. Moreover, we extend this work by considering utility value or importance of event in each time stamp. Therefore, we propose another new High Utility Time Interval-based Events with Duration, HU-TIED, pattern. HU-TIED incorporates the concept of utility pattern mining and TIED pattern mining. We design an algorithm, LMSpan, to mine top-K HU-TIED patterns. For the performance evaluation, we design a baseline algorithm, GenerateNCheck to compare with LMSpan. LMSpan takes less time and memory and generates less candidates than GenerateNCheck.

原文English
頁(從 - 到)1331-1359
頁數29
期刊Knowledge and Information Systems
61
發行號3
DOIs
出版狀態Published - 2019 12月 1

All Science Journal Classification (ASJC) codes

  • 軟體
  • 資訊系統
  • 人機介面
  • 硬體和架構
  • 人工智慧

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