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On the discovery of spatial-temporal fluctuating patterns

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Abstract

In this paper, we explore a new mining paradigm, called spatial-temporal fluctuating patterns (abbreviated as STFs), to discover potentially fluctuating and useful feature sets from the spatial-temporal data. These feature sets have some properties which are variant as time advances. Once STFs are discovered, we can find the turning points of patterns, which enables anomaly detection and transformation discovery over time. For example, the discovery of STFs can possibly figure out the phenomenon of virus variation during the epidemic outbreak, further providing the government with clues for the epidemic control. Therefore, we develop a union-based mining with the downward-closure structure to speed up the spatial-temporal mining process and dynamically compute fluctuating patterns. As shown in our experimental studies, the proposed framework can efficiently discover STFs on a real epidemic disease dataset, showing its prominent advantages to be utilized in real applications.

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Notes

  1. Please refer to https://www.denvergov.org/opendata/dataset/city-and-county-of-denver-crime for a catalog of open data in Denver, CO, US.

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Acknowledgements

This work was supported in part by Ministry of Science and Technology, R.O.C., under Contract 107-2221-E-006 -165-MY2, 107-2218-E-006-040 and 107-2321-B-006-017.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan

    Shan-Yun Teng, Cheng-Kuan Ou & Kun-Ta Chuang

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  1. Shan-Yun Teng
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Correspondence to Kun-Ta Chuang.

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Teng, SY., Ou, CK. & Chuang, KT. On the discovery of spatial-temporal fluctuating patterns. Int J Data Sci Anal 8, 57–75 (2019). https://doi.org/10.1007/s41060-018-0159-1

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