Clustering

Question 1

WCSS

Answer 1

WCSS (Within Cluster Sums of Squares) measures the variability within each cluster. It calculates the sum of squared differences between each data point and its respective cluster centroid. Lower WCSS values indicate that the clusters are tight and data points within the cluster are similar.

Question 2

Silhouette Score

Answer 2

The Silhouette score considers both cohesion (how close data points in a cluster are to each other) and separation (how distinct a cluster is from other clusters).

Question 3

Elbow Method

Answer 3

The Elbow method is a technique used to determine the optimal number of clusters (K) in K-Means clustering. It involves plotting the WCSS against the number of clusters and looking for an “elbow” point where the WCSS starts to decrease more slowly.

Question 4

Recency

Answer 4

The recency variable measures the time elapsed since the last occurrence of an event. In customer segmentation, it is often used to determine how recently a customer made a purchase.