Determination of users’ behavioral models from datasets of users’ session data relates to the clustering task within the application-level web data mining process. The well-known problem of clustering methods is instability in results for different situations. Therefore, evaluation of clustering validity is an essential step. Internal and external cluster validity indices exist, but they cannot replace the evaluation of a human expert in a certain business domain. This paper represents the results of a human expert’s evaluation of two clustering methods – the clickstream method with the Louvain algorithm and the agglomerative sequence alignment method with the hierarchical agglomerative clustering algorithm – applied to a real-world dataset. The evaluation process is done in three iterations. The findings prove the suitability of the clickstream method for this task and discover additional steps to incorporate a human expert’s knowledge into the data transformation process. The results of this research can be used to solve similar tasks on users’ behavioral model detection from session data and to improve the process of data mining.