Abstract: Extraction of fuzzy rules from numerical data for fuzzy modeling and control is significant. Such a problem has received considerable attention in the past and some algorithms, termed as the WM algorithm and the iWM algorithm, have been proposed in the literature. Research on the WM algorithm and the iWM algorithm showed that some improvements on robustness and completeness of these algorithms could be done. This paper aims to develop an improved fuzzy rule extraction algorithm (termed as the DM algorithm) using data mining techniques, and the completeness and the robustness of rule-base for fuzzy modeling with noisy data are addressed. Some illustrative examples are given. Results demonstrate that our proposed rule extraction algorithm outperforms the WM algorithm and iWM algorithm in terms of both modeling accuracy and robustness with respect to noisy data.