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Analyzing parallel programs has become increasingly difficult due to the immense amount of information collected on large systems. In this scenario, cluster analysis has been proved to be a useful technique to reduce the amount of data to analyze. A good example is the use of the density-based cluster algorithm DBSCAN to identify similar single program multiple data (SPMD) computing phases in message-passing applications. This structure detection simplifies the analyst work as the whole information available is reduced to a small set of clusters. However, DBSCAN presents two major problems: it is very sensitive to its parametrization and is not capable of correctly detect clusters when the data set has different densities across the data space. In this paper, we introduce the Aggregative Cluster Refinement, an iterative algorithm that produces more accurate structure detections of SPMD phases than DBSCAN. In addition, it is able to detect clusters with different densities.