Subtractive clustering based methods have been well known for data clustering problems. However

due to the computational demands of these approaches

clustering for large scale datasets

such as spatio-temporal data and images

have been slow to appear. A novel subtractive clustering method based on Nyström approximation is proposed. The proposed method is based on the famous Nyström method. Combined with the density value computation characteristics for each sample of the classical subtractive clustering method

we apply Nyström theory to approximate the density value for each data point which has not been sampled. Finally

we complete the whole clustering procedure using classical subtractive clustering method in modifying the density values in each circulation. The proposed method substantially reduces the computational requirements of subtractive clustering based algorithms

making it feasible to use subtractive clustering to large scale subtractive clustering problems. Density value of samples could be approximated quickly using only a small number of samples. The experiment results on artificial datasets

color images

and UCI machine learning repository show efficiency in comparing with classical subtractive clustering method.