Abstract: This paper discussed the problem that various distances in the interval-data fuzzy c-means clustering method (labeled IFCM) can’t represent the relative position of interval data, and proposed the relative position dissimilarity. The relative position dissimilarity is constructed based on the fact that the differential value between distance of midpoint of interval data and sum of the half length of interval data could reflect the relative position of interval data. And the relative position dissimilarity satisfies the conditions: 1) it decreases as the decrease of the differential value; 2) it decreases as the increase of the sum of interval data length. In theory, the relative position dissimilarity depicts the difference of the interval data in quantity and the relative position of interval data. Meanwhile, the relative position dissimilarity was applied in the IFCM clustering method, which called as IFCM-RPD clustering method. Experimental results show that the IFCM-RPD clustering method has better clustering effect. As well, selection criteria of the parameters in the relative position dissimilarity are given.