The analysis of numerous and intricate data sets is a highly challenging task, in which the technique to detect outliers in data plays a pivotal role. Capturing anomalies by clustering is the most common method among the increasingly popular anomaly detection techniques. This study proposes an anomaly detection algorithm based on second-order proximity (SOPD), which includes clustering and anomaly detection stages. During clustering, the similarity matrix is obtained by second-order proximity. During anomaly detection, the relationships between points in the cluster and the center of the cluster are employed to calculate the distance of all the points in each cluster generated by clustering from the center of the cluster and capture the anomalous state. The density of each data point is also taken into account to exclude the cases of cluster boundaries. The use of second-order proximity enables the locality and globality of the data to be considered simultaneously, which reduces the number of the obtained clusters and increases the accuracy of anomaly detection. Moreover, this study compares this algorithm with some classical anomaly detection algorithms through massive experiments, and the result shows that the SOPD-based algorithm performs well overall.