The identification of crime series is of great importance for public safety in a smart city development. This research presents a novel crime clustering model, CriClust${}^{+}$, for detecting Crime Series Pattern (CSP). The analysis is augmented using geometric projection with a dual-threshold model. The pattern prevalence information extracted from the model is encoded in similarity graphs. Clusters are identified by finding highly-connected subgraphs using adaptive graph size and Monte-Carlo heuristics in the Karger–Stein mincut algorithm. We propose two new interest measures: (i) Proportion Difference Evaluation (PDE), which reveals the propagation effect of a series and dominant series; and (ii) Pattern Space Enumeration (PSE), which reveals strong underlying correlations and defining features for a series. Our findings on experimental dataset based on a Gaussian distribution and expert knowledge recommendation reveal that, identifying CSP and statistically interpretable patterns could contribute significantly to strengthening public safety service delivery. Evaluation was conducted to investigate: (i) the reliability of the model in identifying all inherent series in a crime dataset; (ii) the scalability of the model with varying crime records volume; and (iii) unique features of the model compared to related research. The study also found that PDE and PSE of series clusters can provide valuable insight into crime deterrence strategies. This research presents considerable empirical evidence, which shows that the proposed crime clustering (CriClust${}^{+}$) model is promising and can assist in deriving useful crime pattern knowledge.

犯罪系列的识别对于智慧城市发展中的公共安全至关重要。这项研究提出了一种新颖的犯罪聚类模型CriClust${}^{+}$，用于检测犯罪系列特征码（CSP）。使用带有双阈值模型的几何投影来增强分析。从模型中提取的模式流行度信息被编码在相似图中。通过在Karger-Stein mincut算法中使用自适应图大小和蒙特卡罗启发法找到高度连通的子图，可以识别出聚类。我们提出了两个新的兴趣度量：（i）比例差异评估（PDE），它揭示了一个系列和主要系列的传播效应；（ii）模式空间枚举（PSE），它揭示了强大的潜在相关性并为系列定义了特征。我们基于高斯分布和专家知识推荐的实验数据集发现，识别CSP和统计上可解释的模式可以大大有助于加强公共安全服务的提供。进行了评估以调查：（i）该模型在识别犯罪数据集中所有固有序列时的可靠性；（ii）犯罪记录数量各异的模型的可扩展性；（iii）与相关研究相比，该模型的独特功能。研究还发现，系列聚类的PDE和PSE可以为犯罪威慑策略提供有价值的见解。这项研究提供了大量的经验证据，表明拟议的犯罪聚类（CriClust （ii）犯罪记录数量各异的模型的可扩展性；（iii）与相关研究相比，该模型的独特功能。研究还发现，系列聚类的PDE和PSE可以为犯罪威慑策略提供有价值的见解。这项研究提供了大量的经验证据，表明拟议的犯罪聚类（CriClust （ii）犯罪记录数量各异的模型的可扩展性；（iii）与相关研究相比，该模型的独特功能。研究还发现，系列聚类的PDE和PSE可以为犯罪威慑策略提供有价值的见解。这项研究提供了大量的经验证据，表明拟议的犯罪聚类（CriClust${}^{+}$）模式很有希望，并且可以协助推导有用的犯罪模式知识。