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Wireless link scheduling in device-to-device (D2D) networks is an NP-hard problem. As a solution, multiple supervised deep learning (DL) models have been recently proposed, which depend on the geographical information of D2D pairs. However, such DL models require labeled training data. In this paper, we focus on unsupervised learning of scheduling. More specifically, this paper proposes using a Density-Based anti-Clustering for Scheduling D2D Communications (DBSCHedule). The proposed algorithm is a two-step approach that consists of clustering and anti-clustering. First, clustering aims at identifying the non-interfering groups of D2D pairs. Then, anti-clustering aims at identifying the maximally separated sub-groups to minimize the interference. The clustering step uses a fully-automated unsupervised density-based spectral-clustering of applications with noise (DBSCAN) and the anti-clustering uses the inverse of the objective function of the k-means clustering. Results show comparable performance with the optimal FPLinQ scheduler yet without requiring any channel information nor is there a requirement to solve a complex optimization problem. Moreover, a comparable performance to the previous attempts using DL and modified clustering is achieved while being completely adaptive and easily accommodating to changes in the network layout. [ABSTRACT FROM AUTHOR]