What does the term 'elbow point' refer to in clustering technique?

The term 'elbow point' refers to the optimal cluster number in a graph, particularly when using methods like the K-means clustering algorithm. In clustering, the elbow method provides a visual way to assess the number of clusters to use by plotting the explained variance as a function of the number of clusters. As the number of clusters increases, the explained variance typically rises, but at a certain point, the increase becomes marginal. This point, resembling an elbow in the graph, highlights the trade-off between the number of clusters and the variance explained. Selecting the number of clusters at the elbow point helps avoid overfitting while ensuring that the clusters are well-formed and meaningful.

This concept is crucial for ensuring that the model is not too complex and captures only the essential structures in the data. The elbow point signifies a balance, reflecting a point where adding additional clusters results in diminishing returns in terms of variance explained. Such insights assist data analysts in making informed decisions when setting the number of clusters, promoting efficient use of computational resources and enhancing interpretability of the results.