In this paper we continue our efforts to evaluate matrix clustering algorithms. In our previous study we presented a test environment and results of preliminary experiments with the “separate” strategy for vertical partitioning. This strategy assigns a separate vertical partition for every cluster found by the algorithm, including inter-submatrix attribute group. In this paper we introduce two other strategies: the “replicate” strategy, which replicates inter-submatrix attributes to every cluster and the “retain” strategy, which assigns inter-submatrix attributes to their original clusters. We experimentally evaluate all strategies in a disk-based environment using the standard TPC-H workload and the PostgreSQL DBMS. We start with the study of record reconstruction methods in the PostgreSQL DBMS. Then, we apply partitioning strategies to three matrix clustering algorithms and evaluate both query performance and storage overhead of the resulting partitions. Finally, we compare the resulting partitioning schemes with the ideal partitioning scenario.