Panel data, also known as longitudinal data, is collected and analysed across various research areas. This type of data consists of statistical objects that are periodically observed over time. In comparison to cross-sectional data, there are significantly fewer clustering techniques available for panel data. Therefore, the main objective of the contribution is to present a two-step clustering approach. In the first step, the panel data are transformed into a static form using a set of proposed characteristics that capture time dynamics. In the second step, the objects are clustered using conventional spatial clustering algorithms, such as K-means clustering or hierarchical partitioning. The clustering performance of this approach is then compared to that of the well-known KML method using real panel data sets. These datasets include indicators that assess the effectiveness of courts at the first instance level. Factors like digitalisation in the public sector are affecting the judiciary's efficiency during this period. The methodology implemented allowed us to categorise European countries based on the efficiency of their courts while capturing the dynamic trends. This approach is generally helpful in assessing and comparing the efficiency of public spending and evaluating the quality of public institutions, including courts.

VEGA 1/0411/24 Vplyv ekonomických nástrojov verejnej politiky na digitálnu pripravenosť podnikov European Union's Horizon Europe 101079219 The Brridge project

In: Economic Research Guardian = EcRG. Dumbrăvița : Weissberg SRL, 2025. ISSN 2247-8531. Vol. 15, no. 1 (2025), pp. 142-160.