Scalable clustering based on enhanced-SMART for large-scale FMRI datasets

In this paper, we propose a scalable clustering paradigm to address the problems of excessive computational load and limited clustering performance in large-scale data. The proposed method employs the enhanced splitting merging awareness tactics (E-SMART) algorithm. The large-scale dataset is divided into many sub-datasets sampled randomly from original data. These sub-datasets are clustered using E-SMART with the number of clusters K detected automatically and the resulting partitions are combined and re-clustered. We evaluate our method using synthetic fMRI datasets with different noise levels and one real fMRI dataset. Results show that the accuracy and execution time outperforms the traditional clustering algorithms in large-scale datasets.