Discovery of Spatiotemporal chaining patterns

Bo Heng Chen, Ai Wei Chuang, Kun Ta Chuang

研究成果: Conference contribution

摘要

Spatiotemporal pattern mining attempts to discover unknown, potentially interesting and useful event sequences where events occur within a specific time interval and region. Previous works use partition-based or ill-defined representation of spatial objects which will miss some spatial properties in original spatiotemporal data. Moreover, the problem of non-transactional spatiotemporal database can not be resolved by traditional sequential pattern mining. In this paper, we propose an practical approach to retain the disappearance of spatial correlation which is caused by improper data representation, called Spatiotemporal Frequent Pattern Mining (abbreviated as STFPM), to discover frequent sequential spatiotemporal pattern. Finally, with a case study of crime pattern analysis, our experimental studies show that the proposed (STFPM) framework effectively discovers high-quality spatiotemporal patterns.

原文English
主出版物標題Proceedings of the ASE BigData and SocialInformatics 2015, ASE BD and SI 2015
發行者Association for Computing Machinery
ISBN(電子)9781450337359
DOIs
出版狀態Published - 2015 十月 7
事件ASE BigData and SocialInformatics, ASE BD and SI 2015 - Kaohsiung, Taiwan
持續時間: 2015 十月 72015 十月 9

出版系列

名字ACM International Conference Proceeding Series
07-09-Ocobert-2015

Other

OtherASE BigData and SocialInformatics, ASE BD and SI 2015
國家/地區Taiwan
城市Kaohsiung
期間15-10-0715-10-09

All Science Journal Classification (ASJC) codes

  • 軟體
  • 人機介面
  • 電腦視覺和模式識別
  • 電腦網路與通信

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