The KMeans algorithm clusters data by separating samples into several clusters, characterized by their centers ("centroids"). The algorithm tries to group the data as close as possible to their centroid, by minimizing a criterion called 'inertia'.
The maximum number of iterations to learn the model. KMeans is an iterative algorithm, with each iteration running on all of the data. A higher value of this parameter will lead to a longer running time, but a more precise clustering. A value between 10 and 100 is recommended.
The method to choose initial centroids. Random will select random points, Furthest will try to select point which are far apart, and PlusPlus will use a heuristic to sample points iteratively in order to maximize their mutual distance (also known as "KMeans++").
Used to generate reproducible results. 0 or no value means that no known seed is used (results will not be fully reproducible)