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An innovative model to mine asynchronous periodic pattern of moving objects

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Abstract

Periodic detection in spatiotemporal data is one of the research focuses in data mining. Most previous works only focused on mining periodic patterns and hardly recognized misaligned presence of a pattern due to the intervention of repetitive data. A more flexible asynchronous periodic patterns mining model (AP2M2) based on clustering algorithm and SMCA algorithm is proposed which mainly has three steps, discovering the invisible repetitive data and clustering them into a single record to generate standard and usable dataset, finding the set of stopovers and mining the periodic patterns of moving objects at each stopover. In the experiment, the Chinese bird-watching data is used to check the effectiveness of AP2M2 model and the results show that the AP2M2 model can precisely mine the asynchronous periodic patterns with low time complexity.

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Acknowledgements

This work was supported by China Postdoctoral Science Foundation (No. 2016 M592697) and Key Science and Technology Project of Shandong Province of China (No. 2014GGH201022).

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Authors and Affiliations

  1. School of Data and Computer Science, Shandong Women’s University, Jinan, China

    Shuxia Dong & Zengzhen Shao

  2. School of Information Science and Engineering, Shandong Normal University, Jinan, China

    Shuxia Dong, Shulei Liu, Yanyu Zhao & Zengzhen Shao

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  1. Shuxia Dong
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  2. Shulei Liu
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  4. Zengzhen Shao
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Correspondence to Shuxia Dong.

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Dong, S., Liu, S., Zhao, Y. et al. An innovative model to mine asynchronous periodic pattern of moving objects. Multimed Tools Appl 78, 8943–8964 (2019). https://doi.org/10.1007/s11042-018-6752-4

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