The unsupervised analysis of gene expression data plays a very important role in Genetics experiments. That is why a lot of clustering and biclustering techniques have been proposed. Our choice of biclustering methods is motivated by the accuracy in the obtained results and the possibility to find not only rows or columns that provide a partition of the dataset but also rows and columns together. Unfortunately, the experimental data yet contains many inaccuracy and errors, therefore the main task of mathematicians is to find algorithms that permit to analyze this data with maximal precision. In this work, a new biclustering algorithm that permits to find biclusters with an error less than a predefined threshold is presented. The comparison with other known biclustering algorithms is shown.