  Machine Learning approaches like clustering methods deal with massive
datasets that present an increasing challenge. We devise parallel algorithms to
compute the Multi-Slice Clustering (MSC) for 3rd-order tensors. The MSC method
is based on spectral analysis of the tensor slices and works independently on
each tensor mode. Such features fit well in the parallel paradigm via a
distributed memory system. We show that our parallel scheme outperforms
sequential computing and allows for the scalability of the MSC method.
