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Traditional pattern mining methods generate exhaustive result sets, often overwhelming users with irrelevant patterns—especially in spatial data analysis. The Adaptive Pattern Exploration System (APES) addresses this by interactively optimizing the search process in real time. Instead of returning all possible patterns, APES employs a probabilistic model that dynamically refines the search space based on user feedback, prioritizing relevant results and significantly reducing cognitive and computational burdens.

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APES: Interactive Pattern Mining System Based on Cross-Entropy Probability Modeling

  • 会议论文

  • 发表日期：

    2026年

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  • Shuaikang Yuan,
  • Xuguang Bao,
  • Liang Chang,
  • Tianlong Gu

摘要

Traditional pattern mining methods generate exhaustive result sets, often overwhelming users with irrelevant patterns—especially in spatial data analysis. The Adaptive Pattern Exploration System (APES) addresses this by interactively optimizing the search process in real time. Instead of returning all possible patterns, APES employs a probabilistic model that dynamically refines the search space based on user feedback, prioritizing relevant results and significantly reducing cognitive and computational burdens.