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Cluster validation helps assess the quality and reliability of clustering results.

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It involves evaluating how well a clustering algorithm has performed in grouping similar data points together while keeping dissimilar points in different clusters.

In the previous section we learned about several different algorithms that can be used for clustering. Validation helps us check and compare how these algorithms have performed.

Various metrics and indices have been developed to quantify the effectiveness of clustering solutions.

These validation measures typically focus on two main aspects:

• internal validation (which examines the clustering structure using only the data itself)
• external validation (which compares clustering results against known class labels when available).

Content map

To learn more about methods of validating clusters, read each of the following sections:

Silhouette Score

Davies-Bouldin Index

Calinski-Harabasz Index

Dunn Index

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This is the end of the topic ‘Cluster Analysis’

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Next topic in the module:

Multiple Regression