Background and Context: Source code plagiarism is a common occurrence in undergraduate computer science education. Many source code plagiarism detection tools have been proposed to address this problem. However, such tools do not identify plagiarism, nor suggest what assignment submissions are suspicious of plagiarism. Source code plagiarism detection tools simply evaluate and report the similarity of assignment submissions. Detecting plagiarism always requires additional human intervention. Objective: This work presents an approach that enables the automated identification of suspicious assignment submissions by analysing similarity scores as reported by source code plagiarism detection tools. Method: Density-based clustering is applied to a set of reported similarity scores. Clusters of scores are used to incrementally build an association graph. The process stops when there is an oversized component found in the association graph, representing a larger than expected number of students plagiarising. Thus, the constructed association graph represents groups of colluding students. Findings: The approach was evaluated on data sets of real and simulated cases of plagiarism. Results indicate that the presented approach can accurately identify groups of suspicious assignment submissions, with a low error rate. Implications: The approach has the potential to aid instructors in the identification of source code plagiarism, thus reducing the workload of manual reviewing.