In particle physics, the task of identifying a new signal of interest, to be discriminated from the background process, shall be in principle formulated as a clustering problem. However, while the the signal is unknown, usually even missing, the background process is known and always present. Thus, available data have two different sources: an unlabelled sample which might include observations from both the processes, and an additional labelled, sample from the background only. In this context, semisupervised techniques are particularly suitable to discriminate the two class labels; they lies between unsupervised and supervised ones, sharing some characteristics of both the approaches. In this work we propose a procedure where additional information, available on the background, is integrated within a nonparametric clustering framework to detect deviations from known physics. Also, we propose a variable selection procedure that allows to work on a reduced subspace.