Contents
How to interpret clusters based on decision tree?
Based on decision tree, we can interpret the clusters as follows Cluster 1 — Customer with low to medium total charges, but with internet service Cluster 2 — Customer with low to medium total charges, but without internet service The two approaches above inform us that there are multiple interpretation of clusters.
How to create, visualize and interpret cluster analysis?
One approach is to simply plot all variables and see where the differences are between clusters. This approach, however, fails when dealing with more than 10 variables as it would be difficult to visualize and interpret: The solution would be to select a subset of variables that, to a certain extent, are important when defining clusters.
How is the silhouette score used in cluster analysis?
Although many definitions and methods exist for evaluating clusters, one of the most frequently used methods is calculating something called the Silhouette score. The Silhouette score measures the separability between clusters based on the distances between and within clusters.
What is the goal of Ward’s method in clustering?
Ward’s Method: The goal of Ward’s method is to minimize the variance within each cluster. This is done by de\\fning distance as the di\erence in cluster variance by fusing the clusters. Lets play with the following example: 2
What are the different ways of interpreting clusters?
The process of making clusters is more mathematical oriented, however interpreting clusters is a mix of mathematics and intuition. Lets us explore here what are the different ways of understanding and interpreting clusters
How is machine learning used to interpret clusters?
We can then attempt to use machine learning algorithm for classification in order to “machine-learn” the relation between input (data columns or features) and output (cluster). Then interpreting this machine-learned relation between input and output will give us insight into the meaning of clusters
How to see a cluster in a scatter plot?
A scatter plot visualisation of total charges vs tenure is illustrated below. The color of the points indicates the cluster. We see that the clusters can be distinguished from each other and there is not much overlap.