How does the 'elbow method' help in determining the optimal number of clusters in K-means clustering?
- By calculating the average distance between all pairs of clusters
- By comparing the silhouette scores for different numbers of clusters
- By creating a dendrogram of clusters
- By finding the point in the plot of within-cluster sum of squares where the decrease rate sharply shifts
The elbow method involves plotting the explained variation as a function of the number of clusters and picking the elbow of the curve as the number of clusters to use. This 'elbow' is the point representing the optimal number of clusters at which the within-cluster sum of squares (WCSS) doesn't decrease significantly with each iteration.
Loading...
Related Quiz
- In probability, what does an outcome refer to?
- What is the primary purpose of Principal Component Analysis (PCA)?
- Which type of plot is particularly useful for identifying outliers in a dataset?
- What is the purpose of 'normalization' or 'standardization' in the pre-processing step of cluster analysis?
- When the population variance is unknown, a _______ test is typically used.