---------------criteria--------------------------
variance_explained : the sum of the within-cluster-sum-of-squares-of-all-clusters divided by the total sum of squares
WCSSE : the sum of the within-cluster-sum-of-squares-of-all-clusters
dissimilarity : the average intra-cluster-dissimilarity of all clusters (the distance metric defaults to euclidean)
silhouette : the average silhouette width of all clusters (the distance metric defaults to euclidean)
distortion_fK : this criterion is based on the following paper, 'Selection of K in K-means clustering' (https://www.ee.columbia.edu/~dpwe/papers/PhamDN05-kmeans.pdf)
AIC : the Akaike information criterion
BIC : the Bayesian information criterion
Adjusted_Rsquared : the adjusted R^2 statistic
---------------initializers----------------------
optimal_init : this initializer adds rows of the data incrementally, while checking that they do not already exist in the centroid-matrix [ experimental ]
quantile_init : initialization of centroids by using the cummulative distance between observations and by removing potential duplicates [ experimental ]
kmeans++ : kmeans++ initialization. Reference : http://theory.stanford.edu/~sergei/papers/kMeansPP-soda.pdf AND http://stackoverflow.com/questions/5466323/how-exactly-does-k-means-work
random : random selection of data rows as initial centroids
If the mini_batch_params parameter is not NULL then the optimal number of clusters will be found based on the Mini-batch-Kmeans algorithm, otherwise based on the Kmeans. The higher the init_fraction
parameter is the more close the results between Mini-Batch-Kmeans and Kmeans will be.
In case that the max_clusters parameter is a contiguous or non-contiguous vector then plotting is disabled. Therefore, plotting is enabled only if the max_clusters parameter is of length 1.
Moreover, the distortion_fK criterion can't be computed if the max_clusters parameter is a contiguous or non-continguous vector ( the distortion_fK criterion requires consecutive clusters ).
The same applies also to the Adjusted_Rsquared criterion which returns incorrect output.