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Clustering is an unsupervised machine learning method to divide given data into groups based solely on the features of each sample. Sorting data into clusters can help identify unknown similarities between samples or reveal outliers in the data set. In the real world, clustering has significance across diverse fields from marketing to biology: Clustering applications include market segmentation, social network analysis, and diagnostic medical imaging.

Because this process is unsupervised, multiple clustering results can form around different features. For example, imagine you have a data set composed of various images of red trousers, black trousers, red shirts, and black shirts. One algorithm might find clusters based on clothing shape, while another might create groups based on color.

When analyzing a data set, we need a way to accurately measure the performance of different clustering algorithms; we may want to contrast the solutions of two algorithms, or see how close a

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