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Gridwave: a grid-based clustering algorithm for market transaction data based on spatial-temporal density-waves and synchronization

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Abstract

The notion of density has been widely used in many spatial-temporal (ST) clustering methods. This paper proposes the novel notion of an ST density-wave, which is an extension of the notion of density. It also presents a new grid-based ST clustering algorithm called Gridwave based on the notion of ST density-waves and ST synchronization. The proposed algorithm can be used to discover synchronized changes in density among various locations as well as distinguish ST events and noise from market transaction data. Based on the theory of small-world networks, our algorithm can be used to evaluate ST synchronized correlations among regions with respective to the ST density over the whole network. To improve its performance, the proposed algorithm was implemented using parallel computing. To verify its feasibility, a real large-scale market transaction dataset was used to demonstrate the ST synchronized correlations and the final clustering results. Although our algorithm is applied in a domain-specific case, we suggest that the clustering notion and method could be generalized for other domain applications with similar ST data.

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Acknowledgements

This work was supported in part by the Chinese Universities Scientific Fund, project number 2017XD001. The authors would also like to thank China Tobacco Guangxi Industrial Co., Ltd., for supporting this research.

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Authors and Affiliations

  1. China Agricultural University, 17 Tsinghua East Road, Beijing, China

    Chao Deng, Ruizhi Sun, Saihua Cai & Yinxue Shi

  2. China Tobacco Guangxi Industrial Co., Ltd., 28 Beihu South Road, Nanning, China

    Chao Deng

  3. National Space Science Center of CAS, No. 1 Nanertiao, Zhongguancun, Beijing, China

    Jinwei Song

Authors
  1. Chao Deng
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  2. Jinwei Song
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  3. Ruizhi Sun
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  4. Saihua Cai
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  5. Yinxue Shi
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Correspondence to Ruizhi Sun.

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Deng, C., Song, J., Sun, R. et al. Gridwave: a grid-based clustering algorithm for market transaction data based on spatial-temporal density-waves and synchronization. Multimed Tools Appl 77, 29623–29637 (2018). https://doi.org/10.1007/s11042-017-5441-z

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  • DOI: https://doi.org/10.1007/s11042-017-5441-z

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