用蒙特卡洛树搜索自动发现数据中的有趣模式
Unveiling Interesting Insights: Monte Carlo Tree Search for Knowledge Discovery
- 基于蒙特卡洛树搜索构建探索框架，自动寻找数据转换与模型
- 在真实与合成数据上均有效识别出隐藏的数据模式
- 框架可扩展，适合需要自动化洞察的业务分析场景
组织越来越重视利用流程数据获取洞察并支持决策。然而，将数据转化为可操作的知识仍是一项困难且耗时的任务。数据量与处理能力之间常存在鸿沟，自动化知识发现旨在填补这一空白。该任务涉及复杂挑战，包括有效导航数据、构建模型以提取隐含关系，以及考虑主观目标与知识。本文提出一种新型自动化洞察与数据探索方法（AIDE），通过蒙特卡洛树搜索（MCTS）实现对这些挑战的稳健应对。我们在真实世界和合成数据上评估了AIDE，证明其能有效识别出揭示有趣数据模式的数据变换与模型。AIDE的MCTS框架具备显著可扩展性，未来可集成更多模式提取策略与领域知识，是迈向全面自动化知识发现的重要一步。
原文摘要 · Abstract (English)
Organizations are increasingly focused on leveraging data from their processes to gain insights and drive decision-making. However, converting this data into actionable knowledge remains a difficult and time-consuming task. There is often a gap between the volume of data collected and the ability to process and understand it, which automated knowledge discovery aims to fill. Automated knowledge discovery involves complex open problems, including effectively navigating data, building models to extract implicit relationships, and considering subjective goals and knowledge. In this paper, we introduce a novel method for Automated Insights and Data Exploration (AIDE), that serves as a robust foundation for tackling these challenges through the use of Monte Carlo Tree Search (MCTS). We evaluate AIDE using both real-world and synthetic data, demonstrating its effectiveness in identifying data transformations and models that uncover interesting data patterns. Among its strengths, AIDE's MCTS-based framework offers significant extensibility, allowing for future integration of additional pattern extraction strategies and domain knowledge. This makes AIDE a valuable step towards developing a comprehensive solution for automated knowledge discovery.
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