In the context of Cluster metrics, what does "Coherence" measure?

Coherence in the context of cluster metrics refers to the measure of document similarity within a cluster. It assesses how closely aligned or related the documents in a specific cluster are to one another. High coherence indicates that the documents share common themes or topics, making them collectively meaningful and relevant to the cluster's label. This is vital for ensuring that the clusters accurately represent distinct topics or concepts, which is essential for effective information retrieval and data analysis.

When clusters have high coherence, users can have confidence that the documents grouped together provide consistent information, making it easier for them to derive insights. Conversely, if coherence is low, it may suggest that the documents are not closely related, which could diminish the utility of the clustering outcome. Thus, coherence serves as a crucial metric for evaluating the quality and effectiveness of clustering algorithms.