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Vertical partitioning is a technique used to reduce disk access, when executing a given set of queries, by minimizing the access to irrelevant instance variables. In this paper we use the FP-Max data mining algorithm, for extracting frequent item set attributes. The frequently accessed instance variables are, then, grouped as vertical class fragments. We study the application of this approach to sets of queries on large databases and data warehouses. We used two benchmarks with various minimum support levels and we compare our results with the results of the approach using the Apriori data mining technique. The partitioning solution obtained produces an improvement of 14% for large data bases and 19% for data warehouse compared to a solution without partitioning.