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One data aggregation method in a wireless sensor network (WSN) is sending local representative data to the sink node based on the spatial-correlation of sampled data. In this paper, we highlight the problem that the recent spatial correlation models of sensor nodes' data are not appropriate for measuring the correlation in a complex environment. In addition, the representative data are inaccurate when compared with real data. Thus, we propose the data density correlation degree, which is necessary to resolve this problem. The proposed correlation degree is a spatial correlation measurement that measures the correlation between a sensor node's data and its neighboring sensor nodes' data. Based on this correlation degree, a data density correlation degree (DDCD) clustering method is presented in detail so that the representative data have a low distortion on their correlated data in a WSN. In addition, simulation experiments with two real data sets are presented to evaluate the performance of the DDCD clustering method. The experimental results show that the resulting representative data achieved using the proposed method have a lower data distortion than those achieved using the Pearson correlation coefficient based clustering method or the α-local spatial clustering method. Moreover, the shape of clusters obtained by DDCD clustering method can be adapted to the environment.