Clustering.Rd
This is a modified kmeans clustering technique to automatize the number of groups or clusters that can be partitioned the sample. Several techniques are used to obtain the best number of clusters.
Clustering(data, n = "auto", n_max = 10, iter.max = 10, auto_criterion = c("explainwss", "db", "ratkowsky", "ball", "friedman"), confidenceWSS = 0.9, agregate_method = median)
data | Data frame which numeric variables. |
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n | Data frame which numeric variables. |
n_max | maximal number of clusters, between 2 and (number of objects - 1), greater or equal to n_min. By default, n_max=10. |
iter.max | the maximum number of iterations allowed. |
auto_criterion | the available criterions are: "explainwss", "db", "ratkowsky", "ball" and "friedman". |
confidenceWSS | a confidence interval for criterion WSS. |
agregate_method | a function to agregate results of different methods. Default value=median |
Several methods are available in order to obtain the best number of clusters: explainwss = Within-cluster Sum of Square db = Davies–Bouldin index (DBI). Davies and Bouldin (1979) ratkowsky = Ratkowsky and Lance (1978) ball = Ball and Hall (1965) friedman = Friedman and Rubin (1967)
@return A MLA object of subclass Clustering
## Load a Dataset# NOT RUN { data(EGATUR) modelFit <- Clustering(data=EGATUR[,c("A13","gastototal")]) # }