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An Improved HotSpot Algorithm and Its Application to Sandstorm Data in Inner Mongolia

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  • Ren Qing-dao-er-ji
  • Rui Pang
  • Yue Chang

Abstract

HotSpot is an algorithm that can directly mine association rules from real data. Aiming at the problem that the support threshold in the algorithm cannot be set accurately according to the actual scale of the dataset and needs to be set artificially according to experience, this paper proposes a dynamic optimization algorithm with minimum support threshold setting: S_HotSpot algorithm. The algorithm combines simulated annealing algorithm with HotSpot algorithm and uses the global search ability of simulated annealing algorithm to dynamically optimize the minimum support in the solution space. Finally, the Inner Mongolia sandstorm dataset is used for experiment while the wine quality dataset is used for verification, and the association rules screening indicators are set for the mining results. The results show that S_HotSpot algorithm can not only dynamically optimize the selection of support but also improve the quality of association rules as it is mining reasonable number of rules.

Suggested Citation

  • Ren Qing-dao-er-ji & Rui Pang & Yue Chang, 2020. "An Improved HotSpot Algorithm and Its Application to Sandstorm Data in Inner Mongolia," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-10, April.
    • Handle: RePEc:hin:jnlmpe:4020723
    DOI: 10.1155/2020/4020723
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    Cited by:

    1. Po-Yuan Shih & Cheng-Ping Cheng & Dong-Her Shih & Ting-Wei Wu & David C. Yen, 2022. "Who Is the Most Effective Country in Anti-Corruption? From the Perspective of Open Government Data and Gross Domestic Product," Mathematics, MDPI, vol. 10(13), pages 1-20, June.

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