Mining frequent time interval-based event with duration patterns from temporal database

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

研究成果: Conference contribution

7 引文 斯高帕斯(Scopus)

摘要

Time interval-based pattern mining is proposed to improve the lack of the information of time intervals by sequential pattern mining. 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 durations will cause definitely different results. For example, if some people cough for one week, they may get a cold for a while. In contrast, if some patients cough for one year, they may get pneumonia in the future. In this work, we propose two algorithms, SARA and SARS, to extract the frequent Time Interval-based Event 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. In the experiments, we propose a naive algorithm and modify a previous algorithm to compare the performances with SARA and SARS. The experimental results show that SARA and SARS are more efficient in execution time and memory usage than other two algorithms.

原文English
主出版物標題DSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics
編輯George Karypis, Longbing Cao, Wei Wang, Irwin King
發行者Institute of Electrical and Electronics Engineers Inc.
頁面548-554
頁數7
ISBN(電子)9781479969913
DOIs
出版狀態Published - 2014 3月 10
事件2014 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2014 - Shanghai, China
持續時間: 2014 10月 302014 11月 1

出版系列

名字DSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics

Other

Other2014 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2014
國家/地區China
城市Shanghai
期間14-10-3014-11-01

All Science Journal Classification (ASJC) codes

  • 人工智慧
  • 資訊系統
  • 資訊系統與管理

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