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Data clustering constitutes at present a commonly used technique for extracting fuzzy system rules from experimental data. Detailed studies in the field have shown that using above-mentioned method results in significantly reduced structure of fuzzy identification system, maintaining at the same time its high modelling efficiency. In this paper a clustering algorithm, based on a kernel density gradient estimation procedure applied for fuzzy models synthesis, is presented. It consists of two stages: data elements relocation and their division into clusters. The method is automatic, unsupervised, and does not require any assumptions concerning the desired number of fuzzy rules. The results of experimental evaluation show that the algorithm under consideration achieves relatively high performance when compared to the standard techniques frequently applied in similar class of problems.