Pattern discovery and subspace clustering are pervasive tasks across biological, biotechnological, and biomedical domains. Parallel coordinates plots and heatmaps are reference visualizations for individual biclusters. Both have been object of improvements over time, with a special emphasis on heatmaps, commonly used in gene expression analysis. However, the emphasis is solely placed on the corresponding subspace, preventing an assessment of biclusters' significance against global regularities. This work proposes an improvement on bicluster visualization by disruptively extending parallel coordinates representations with the means to compare the local bicluster against the remaining dataset instances helping in the contextualization of a pattern in the broader picture of an entire dataset. The proposed solution is the first able to deal with mixed data types and is independent from the underlying biclustering or pattern mining algorithm. Results in different data domains show the utility of the proposed visualization, especially in primary phases where visual inspection of biclusters is used.