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Reinforcement Learning (RL) has gained significant traction in the field of data mining, offering a paradigm shift in how algorithms learn and adapt from interactions with an environment. This paper explores the application of RL in data mining and highlights emerging trends. RL excels in scenarios where systems make sequential decisions, making it suitable for dynamic and evolving data environments. Its ability to optimize decisions over time has found applications in diverse domains such as finance, healthcare, and autonomous systems. In data mining, RL contributes to improved pattern recognition, anomaly detection, and decision-making processes. The paper delves into key applications, including fraud detection, recommendation systems, and resource allocation, showcasing how RL enhances the efficiency and adaptability of data-driven models.

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Reinforcement Learning in Data Mining - Applications and Emerging Trends

  • 会议论文

  • 发表日期：

    2026年

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  • Kasapaka RubenRaju,
  • BommaReddy Sindhuja,
  • Aluka Madhavi,
  • Samala Nandini,
  • Potlakayala Deepthi,
  • Manchala Bhavani

摘要

Reinforcement Learning (RL) has gained significant traction in the field of data mining, offering a paradigm shift in how algorithms learn and adapt from interactions with an environment. This paper explores the application of RL in data mining and highlights emerging trends. RL excels in scenarios where systems make sequential decisions, making it suitable for dynamic and evolving data environments. Its ability to optimize decisions over time has found applications in diverse domains such as finance, healthcare, and autonomous systems. In data mining, RL contributes to improved pattern recognition, anomaly detection, and decision-making processes. The paper delves into key applications, including fraud detection, recommendation systems, and resource allocation, showcasing how RL enhances the efficiency and adaptability of data-driven models.