用机器学习提升犯罪关联分析，构建统一框架助力数据驱动研究
Advancing Crime Linkage Analysis with Machine Learning: A Comprehensive Review and Framework for Data-Driven Approaches
- 梳理多领域文献，提出犯罪关联分析的通用流程框架
- 揭示当前机器学习在犯罪关联中的核心挑战与局限
- 适合对犯罪分析、数据挖掘感兴趣的学者与技术人员
犯罪关联是通过分析犯罪行为数据判断多起案件是否属于同一系列犯罪的过程。该领域长期受到社会学、心理学和统计学研究者的关注。近年来，随着人工智能的发展，计算机科学界也逐渐重视此问题，但相关研究仍处于初期阶段。本文旨在厘清机器学习在犯罪关联分析中面临的挑战，并为未来数据驱动方法奠定基础。为此，我们系统梳理了该领域的主流文献，构建了一个通用的犯罪关联分析框架，详细描述了每个步骤。目标是将跨学科的洞见整合为统一术语体系，促进该领域研究的发展。
原文摘要 · Abstract (English)
Crime linkage is the process of analyzing criminal behavior data to determine whether a pair or group of crime cases are connected or belong to a series of offenses. This domain has been extensively studied by researchers in sociology, psychology, and statistics. More recently, it has drawn interest from computer scientists, especially with advances in artificial intelligence. Despite this, the literature indicates that work in this latter discipline is still in its early stages. This study aims to understand the challenges faced by machine learning approaches in crime linkage and to support foundational knowledge for future data-driven methods. To achieve this goal, we conducted a comprehensive survey of the main literature on the topic and developed a general framework for crime linkage processes, thoroughly describing each step. Our goal was to unify insights from diverse fields into a shared terminology to enhance the research landscape for those intrigued by this subject.
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