$ sudo apt install libcereal-dev libarmadillo-dev libensmallen-dev
Whether to use mlpack or another library or plugin really depends on your specific needs and constraints. Here are some factors to consider:
Functionality: If you need advanced machine learning algorithms beyond k-means clustering, then mlpack may be a good choice as it provides a wide range of machine learning algorithms. If you only need k-means clustering, then there may be simpler or more lightweight libraries or plugins available that can do the job.
Programming language: mlpack is a C++ library, so if you are more comfortable with other programming languages such as Python, R or Java, you may prefer a library or plugin that supports your preferred language.
Integration: If you are already using a particular software platform or framework, such as MATLAB or TensorFlow, it may be easier to use a library or plugin that integrates well with that platform or framework.
Performance: If you need to process large amounts of data or need to achieve high performance, then you may want to consider a library or plugin that is optimized for performance, such as CUDA-accelerated libraries.
In general, mlpack is a powerful and flexible library for machine learning in C++. It has good documentation and a supportive community, so if you need a range of machine learning algorithms in C++, it may be a good choice. However, for simpler tasks such as k-means clustering, there may be simpler or more lightweight libraries or plugins available.
https://cppsecrets.com/users/489510710111510497118107979811497495464103109971051084699111109/C00-MLPACK-KMeans.php https://www.analyticsvidhya.com/blog/2019/08/comprehensive-guide-k-means-clustering/
https://arma.sourceforge.net/docs.html https://stackoverflow.com/questions/64590176/iterating-over-armamat-and-retrieving-element-locations