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I still have a problem though. How can I determine the optimum number of clusters if I have mixed data? These functions require numerical matrices as input. In my data I have nominal, ordinal and numerical variables. I used gower distances to calculate the dissimilarity matrix and was planing to use hierarchical clustering but I am open to change that method. There 4 different other methods other than the elbow method.
The hierarchical method uses Elbow method though. How to fix it? Elbow method Recall that, the basic idea behind partitioning methods, such as k-means clustering, is to define clusters such that the total intra-cluster variation [or total within-cluster sum of square WSS ] is minimized. The optimal number of clusters can be defined as follow: Compute clustering algorithm e. For instance, by varying k from 1 to 10 clusters. For each k, calculate the total within-cluster sum of square wss.
Plot the curve of wss according to the number of clusters k. The location of a bend knee in the plot is generally considered as an indicator of the appropriate number of clusters. The algorithm is similar to the elbow method and can be computed as follow: Compute clustering algorithm e. For each k, calculate the average silhouette of observations a v g. Plot the curve of a v g. The location of the maximum is considered as the appropriate number of clusters. Gap statistic method The gap statistic has been published by R. Generate B reference data sets with a random uniform distribution. Compute also the standard deviation of the statistics.
Note that the hcut function is available only in factoextra package. It computes hierarchical clustering and cut the tree in k pre-specified clusters. It can simultaneously computes all the indices and determine the number of clusters in a single function call. NbClust for computing about 30 methods at once, in order to find the optimal number of clusters.
Allowed values include kmeans, pam, clara and hcut for hierarchical clustering. Elbow method: 4 clusters solution suggested Silhouette method: 2 clusters solution suggested Gap statistic method: 4 clusters solution suggested. Here, there are contents hidden to non-premium members. Sign up now to read all of our premium contents and to be awarded a certificate of course completion. Claim Your Membership Now. According to the majority rule, the best number of clusters is 2. Summary In this article, we described different methods for choosing the optimal number of clusters in a data set. Assessing Clustering Tendency Prev Lesson.
Comments 9 Kate Godfrey. Thank you for your input. Hi, the error is not reproducible on my computer. Post a Reply Cancel reply.