Optimal_Clusters_KMeans(data, max_clusters, criterion = "variance_explained", fK_threshold = 0.85, num_init = 1, max_iters = 200, initializer = "optimal_init", threads = 1, tol = 1e-04, plot_clusters = TRUE, verbose = FALSE, tol_optimal_init = 0.5, seed = 1)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
quantile_init : initialization of centroids by using the cummulative distance between observations and by removing potential duplicates
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
data(dietary_survey_IBS)
dat = dietary_survey_IBS[, -ncol(dietary_survey_IBS)]
dat = center_scale(dat)
opt = Optimal_Clusters_KMeans(dat, max_clusters = 10, plot_clusters = FALSE)
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