K-Means clusternig example with Python and Scikit-learn

Clustering algorithms group a set of documents into subsets or clusters . The algorithms' goal is to create clusters that are coherent internally, but clearly different from each other. In other words, documents within a cluster should be as similar as possible; and documents in one cluster should be as dissimilar as possible from documents in other clusters.

Hierarchical clustering is where the machine is allowed to decide how many clusters to create based on its own algorithms.

After downloading the required modules, run the file. You can play on it as much as you want.

https://pythonprogramming.net/flat-clustering-machine-learning-python-scikit-learn/
https://nlp.stanford.edu/IR-book/html/htmledition/flat-clustering-1.html
https://www.youtube.com/watch?v=ijUMKMC4f9I
https://nlp.stanford.edu/IR-book/pdf/16flat.pdf
https://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.hierarchy.fcluster.html
https://scikit-learn.org/stable/modules/clustering.html