Group Recommendation (GR), which aims to recommend items to groups of users, has become a promising and practical direction for recommendation systems. To this end, we present a novel unsupervised group recommendation framework named \underline{\text{I}} dentify \underline{\text{T}} hen \underline{\text{R}} ecommend ( \underline{\text{ITR}}), where it first identifies the user groups in an unsupervised manner even without the pre-defined number of groups, and then two pre-text tasks are designed to conduct self-supervised group recommendation. Concretely, at the group identification stage, we first estimate the adaptive density of each user point, where areas with higher densities are more likely to be recognized as group centers. Then, a heuristic merge-and-split strategy is designed to discover the user groups and decision boundaries. Subsequently, at the self-supervised learning stage, the pull-and-repulsion pre-text task is proposed to optimize the user-group distribution.