Train and Evaluate Clustering Models (1/2)

Clustering is the process of grouping objects with similar objects.

What is Clustering?

Clustering is a form of unsupervised machine learning in which observations are grouped into clusters based on similarities in their 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 purely 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 this grouping could be done. 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.