In this dissertation adapted methods and new combined method for clustering spatially uncertain data in ℝ3 geometrical space are presented. The proposed combined method uses the advantages and eliminates the disadvantages of the existing methods. Combined method enables faster clustering of objects with spatial uncertainty using simple comparisons mathematical functions for cluster pruning. Pruning additional number of clusters is achived by using bisector planes for comparison and rejection of clusters as potential candidates. The new method significantly speeds up the process of pruning clusters, compared to existing methods. This is achived by minimizing the total number of objects for which it is necessary to calculate the expected distance. A warehouse model for verification of new clusternig method is created. Experiments are carried out with the aim of shortening the time for clustering of products and finding the optimal position for automated servers. Proposals for servis optimizing by customizing warehouse model, for new method, are presented. On the basis of proposals new warehouse model is created. On the new developed model same experiments are performed with the aim of proving acceleration of the clustering process.