  The problem of optimizing distributed database includes: fragmentation and
positioning data. Several different approaches and algorithms have been
proposed to solve this problem. In this paper, we propose an algorithm that
builds the initial equivalence relation based on the distance threshold. This
threshold is also based on knowledge- oriented clustering techniques for both
of horizontal and vertical fragmentation. Similarity measures used in the
algorithms are the measures developed from the classical measures. Experimental
results carrying on the small data set match fragmented results based on the
classical algorithm. Execution time and data fragmentation significantly
reduced while the complexity of our algorithm in the general case is stable.
