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Fast mining of spatial frequent wordset from social database

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Abstract

In this paper, we propose an algorithm that extracts spatial frequent patterns to explain the relative characteristics of a specific location from the available social data. This paper proposes a spatial social data model which includes spatial social data, spatial support, spatial frequent patterns, spatial partition, and spatial clustering; these concepts are used for describing the exploration algorithm of spatial frequent patterns. With these defined concepts as the foundation, an SFP-tree structure that maintains not only the frequent words but also the frequent cells was proposed, and an SFP-growth algorithm that explores the frequent patterns on the basis of this SFP-tree was proposed.

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Acknowledgements

This research was supported by a Grant (14NSIP-B080144-01) from National Land Space Information Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government.

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Authors and Affiliations

  1. Department of Software, Chungbuk National University, Cheongju, South Korea

    Yongmi Lee & Keun Ho Ryu

  2. School of Computer, Information, and Communications Engineering, Kunsan National University, Gunsan, South Korea

    Kwang Woo Nam

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  1. Yongmi Lee
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  2. Kwang Woo Nam
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Correspondence to Kwang Woo Nam or Keun Ho Ryu.

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Lee, Y., Nam, K.W. & Ryu, K.H. Fast mining of spatial frequent wordset from social database. Spat. Inf. Res. 25, 271–280 (2017). https://doi.org/10.1007/s41324-017-0094-6

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  • DOI: https://doi.org/10.1007/s41324-017-0094-6

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