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What is a clustering variable?
Clustering variables allows you to reduce the number of variables for analysis. The scientist wants to reduce the total number of variables by combining variables with similar characteristics. Cluster variables uses a hierarchical procedure to form the clusters.
What is Proc Varclus?
The VARCLUS procedure is a useful SAS procedure for variable reduction. It is based on divisive clustering technique. To determine which inputs are included in each cluster, the principal component scores are rotated obliquely to maximize the correlation within a cluster and minimize the correlation between clusters.
How does the k-means clustering algorithm work?
It tries to make the intra-cluster data points as similar as possible while also keeping the clusters as different (far) as possible. It assigns data points to a cluster such that the sum of the squared distance between the data points and the cluster’s centroid (arithmetic mean of all the data points that belong to that cluster) is at the minimum.
How is clustering used in exploratory data analysis?
Clustering is one of the most common exploratory data analysis technique used to get an intuition about the structure of the data. It can be defined as the task of identifying subgroups in the data such that data points in the same subgroup (cluster) are very similar while data points in different clusters are very different.
How to assign data points to the closest cluster?
Assign each data point to the closest cluster (centroid). Compute the centroids for the clusters by taking the average of the all data points that belong to each cluster. The approach kmeans follows to solve the problem is called Expectation-Maximization. The E-step is assigning the data points to the closest cluster.
How to determine the degree of separation between clusters?
Silhouette analysis can be used to determine the degree of separation between clusters. For each sample: Compute the average distance from all data points in the same cluster (ai). Compute the average distance from all data points in the closest cluster (bi). Compute the coefficient: