What is clustering?
Clustering is a form of unsupervised machine learning in which observations are grouped into clusters based on similarities in their data values, or features. This kind of machine learning is considered unsupervised because it doesn't make use of previously known label values to train a model. In a clustering model, the label is the cluster to which the observation is assigned, based only on its features.
For example, suppose a botanist observes a sample of flowers and records the number of petals and leaves on each flower.
It may be useful to group these flowers into clusters based on similarities between their features.
There are many ways to determine this grouping. For example, if most flowers have the same number of leaves, they could be grouped into flowers with many vs few petals. Alternatively, if both petal and leaf counts vary considerably there may be a pattern to discover, such as flowers with many leaves also having many petals. The goal of the clustering algorithm is to find the optimal way to split the dataset into groups. What optimal means depends on both the algorithm used and the dataset that is provided.
Although this flower example is easy to categorize with only a few samples, as the dataset grows to thousands of samples or to more than two features, clustering algorithms become useful to quickly sort out a dataset into groups.