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Clustering is a data mining technique that allows the grouping of data points on the basis of their similarity with respect to multiple dimensions of measurement. It has also been applied in the software engineering domain, in particular to support software quality assessment based on source code metrics. Unfortunately, since clusters emerge from metrics at the source code level, it is difficult to interpret the significance of clusters at the level of the quality of the entire system. In this paper, we propose a method for interpreting source code clusters using the ISO/IEC 9126 software product quality model. Several methods have been proposed to perform quantitative assessment of software systems in terms of the quality characteristics defined by ISO/IEC 9126. These methods perform mappings of low-level source code metrics to high-level quality characteristics by various aggregation and weighting procedures. We applied such a method to obtain quality profiles at various abstraction levels for each generated source code cluster. Subsequently, the plethora of quality profiles obtained is visualized such that conclusions about different quality problems in various clusters can be obtained at a glance.