Hey!
Hierarchical clustering can be memory-intensive due to the computation of pairwise distances, particularly with large datasets like yours. As a result, you're encountering memory issues.
One approach to alleviate this is to reduce the size of your dataset and consider using a random sample of your data if that aligns with your analysis goals. Additionally, performing dimensionality reduction with techniques such as Principal Component Analysis (PCA) can help by reducing the number of features.
Moreover, hierarchical clustering might not be the most efficient choice for large datasets. Methods like "kmeans" or "kmedoids" are generally more memory-efficient and could be suitable alternatives depending on your analysis needs. If possible, running your code on a machine with more RAM could also help manage the memory load.
If you have MATLAB's Parallel Computing Toolbox, consider using "tall" arrays, which are designed to handle large datasets that don't fit into memory. Additionally, clearing unnecessary variables with ">> clearvars" and checking available memory with the ">> memory" command can help manage your workspace more effectively.
