| Determination of users’ behavioral models from datasets of users’ session da-ta relates to the clustering task within the application-level web data mining process. The well-known problem of clustering methods is instability in re-sults 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 do-main. This paper represents the results of a human expert’s evaluation of two clustering methods – the clickstream method with the Louvain algo-rithm and the agglomerative sequence alignment method with the hierar-chical 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 transfor-mation 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. |
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