When processing a multi-view, epilepsy electroencephalogram (EEG) dataset, the traditional single-view clustering algorithms cannot fully mine the correlation information between each view and identify the importance of each view because of the limitations of its own methods. This limitation causes poor clustering performance when using these classic, single-view clustering algorithms. To solve this problem, a novel double-index-constrained, multi-view, fuzzy clustering algorithm (DIC-MV-FCM) is proposed for the automatic detection of epilepsy EEG data. The DIC-MV-FCM algorithm is integrated into the multi-view clustering technology and the view-weighted adaptive learning strategy, which can effectively use the correlation information between each view and control the importance of each view to improve the final clustering performance. The experimental results using several epilepsy EEG datasets show that the proposed DIC-MV-FCM algorithm has better clustering performance than the traditional clustering algorithms for processing multi-view EEG data.