Binning constitutes a crucial step of de novo metagenomics data analysis, and several promising attempts to partially automate this process have been proposed; quite a few recent approaches rely on machine learning techniques, in particular clustering. However, so far, there does not exist a fully automated process, nor a thorough evaluation of its accuracy and robustness with respect to parameterisation. This contribution addresses the following issues: (i) an integration of modern dimensionality reduction and clustering techniques suitable for high dimensional data, and an automated selection of the number of clusters, (ii) a formal quantitative evaluation of the pipeline in benchmarks, (iii) and an evaluation of an optimum parameter choice, resulting in a complete automation of the process.