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.

翻译：犯罪关联分析是通过分析犯罪行为数据，以判断一对或一组犯罪案件是否相互关联或属于同一系列犯罪的过程。该领域已受到社会学、心理学和统计学研究者的广泛研究。近年来，随着人工智能技术的进步，计算机科学家对此领域的兴趣也日益增长。尽管如此，文献表明计算机科学领域在此方向的研究仍处于早期阶段。本研究旨在理解机器学习方法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知识基础。为实现这一目标，我们对相关主题的主要文献进行了全面调研，并构建了一个通用的犯罪关联分析流程框架，详细阐述了每个步骤。我们的目标是将来自不同领域的见解统一于共享的术语体系中，以促进对该主题感兴趣的研究者进一步推动该领域的发展。