Current State-of-the-Art High Throughput Satellite systems provide wide-area connectivity through multi-beam architectures. However, due to the tremendous system throughput requirements that next generation Satellite Communications expect to achieve, traditional 4-colour frequency reuse schemes, i.e., two frequency bands with two orthogonal polarisations, are not sufficient anymore and more aggressive solutions as full frequency reuse are gaining momentum. These approaches require advanced interference management techniques to cope with the significantly increased inter-beam interference, like multicast precoding and Multi User Detection. With respect to the former, several peculiar challenges arise when designed for SatCom systems. In particular, to maximise resource usage while minimising delay and latency, multiple users are multiplexed in the same frame, thus imposing to consider multiple channel matrices when computing the precoding weights. In this paper, we focus on this aspect by re-formulating it as a clustering problem. After introducing a novel mathematical framework, we design a k-means-based clustering algorithm to group users into simultaneously precoded and served clusters. Two similarity metrics are used to this aim: the users' Euclidean distance and their channel distance, i.e., distance in the multidimensional channel vector space. Through extensive numerical simulations, we substantiate the proposed algorithms and identify the parameters driving the system performance.