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<p>The rapid growth of spatio-temporal (ST) data from domains such as crime analysis, traffic control, and environmental monitoring calls for efficient pattern mining techniques. Traditional methods often generate redundant patterns, making interpretation challenging. To address this, a Closed Spatio-Temporal Co-occurrence Pattern Mining (C-STCOP) approach that extracts non-redundant patterns for improved decision-making is proposed. This research work introduces two novel measures namely, Coupling Coefficient (CoCo) and Closed Participation Index (CPI), to evaluate the quality of mined patterns. The proposed method is validated on real-world datasets, demonstrating its effectiveness in concise pattern extraction compared to existing approaches.</p>

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Lossless and closed co-occurring pattern mining algorithm for spatio-temporal datasets (C-STCOP)

  • 期刊论文

  • 发表日期：

    2025年4月8日

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  • S. Sharmiladevi,
  • S. Siva Sathya,
  • S. LourduMarie Sophie

摘要

The rapid growth of spatio-temporal (ST) data from domains such as crime analysis, traffic control, and environmental monitoring calls for efficient pattern mining techniques. Traditional methods often generate redundant patterns, making interpretation challenging. To address this, a Closed Spatio-Temporal Co-occurrence Pattern Mining (C-STCOP) approach that extracts non-redundant patterns for improved decision-making is proposed. This research work introduces two novel measures namely, Coupling Coefficient (CoCo) and Closed Participation Index (CPI), to evaluate the quality of mined patterns. The proposed method is validated on real-world datasets, demonstrating its effectiveness in concise pattern extraction compared to existing approaches.