Clustering

Not Ideal For

• Projects requiring tight integration with Python or R data science stacks, as Clustering.jl is Julia-specific and lacks cross-language bindings.
• Applications needing real-time, incremental clustering on streaming data, since the package focuses on batch processing algorithms without built-in streaming support.
• Teams looking for deep learning-based clustering methods, as it includes traditional algorithms but not neural approaches like autoencoder-based clustering.
• Large-scale data analysis requiring distributed or GPU-accelerated clustering, as scalability features are not highlighted in the documentation.