In this work we introduce a novel technique to enumerate constant row/column value biclusters using formal concept analysis. To achieve this, a numerical data-table (standard input for biclustering al-gorithms) is modelled as a many-valued context where rows represent objects and columns represent attributes. Using equivalence relations de-fined for each single column, we are able to translate the bicluster mining problem in terms of the partition pattern structure framework. We show how biclustering can benefit from the FCA framework through its ro-bust theoretical description and efficient algorithms. Finally, we show how this technique is able to find high quality biclusters (in terms of the mean squared error) more efficiently than a state-of-the-art bicluster algorithm.