In a relational database environment, partitioning of data is directly concerned with the amount of data that needs to be required in a query or transaction. In this paper, we consider non-overlapping, vertical partitioning. Vertical partitioning algorithm in this paper is composed of two phases. In phase 1, we cluster the attributes with zero-one integer program that maximize affinity among attributes. The result of phase 1 is called 'Initial Fragments'. In phase 2, we modify Initial Fragments that is not directly considered by cost factors, making use of a transaction-based partitioning method. A transaction-based partitioning method is partitioning attributes according to a set of transactions. In this phase we select logical accesses which needs to be required in a transaction as comparison criteria. In phase 2, proposed algorithm consider only small number of modification of Initial Fragments in phase 1. This algorithm is so insensible to number of transactions and of attributes that it can applied to relatively large problems easily.
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