Evaluating clustering performance is an essential step to assess the strength of a clustering algorithm for a given dataset. Assessing performance in an unsupervised environment is not an easy task, but in the literature, many methods are available. We can categorize these methods into two broad categories: internal and external performance evaluation. Let's learn about both of these categories in detail.
Internal performance evaluation
In internal performance evaluation, clustering is evaluated based on feature data only. This method does not use any target label information. These evaluation measures assign better scores to clustering methods that generate well-separated clusters. Here, a high score does not guarantee effective clustering results.
Internal performance evaluation helps us to compare multiple clustering algorithms but it does not mean that a better-scoring algorithm will generate better results than other algorithms. The following...
