# Introduction Biclustering is a method that simultaneously clusters rows and columns of a data matrix. This allows us to identify submatrices within the data matrix that have specific properties. Biclustering is useful in various domains, including data analysis, image processing, and bioinformatics. In this lab, we will learn how to perform biclustering using the `sklearn.cluster.bicluster` module in scikit-learn. We will explore two common biclustering algorithms: Spectral Co-Clustering and Spectral Biclustering. These algorithms differ in how they define and assign rows and columns to biclusters. ## VM Tips After the VM startup is done, click the top left corner to switch to the **Notebook** tab to access Jupyter Notebook for practice. Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook. If you face issues during learning, feel free to ask Labby. Provide feedback after the session, and we will promptly resolve the problem for you.
Click the virtual machine below to start practicing