A NystrÖm-based subtractive clustering method

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 spatiotemporal data and images have been slow to appear. A novel subtractive clustering method based on NystrÖm approximation is proposed. The contribution of proposed is a method that substantially reduces the computational requirements of subtractive clustering based algorithms, making it feasible to use subtractive clustering to large scale subtractive clustering problems. The proposed method is based on the famous NystrÖm method. All potentials of samples could be approximated quickly using only a litter number of samples. The experiment results on color images show efficiency in comparing with conventional subtractive clustering method.