Hello. In this lesson, you will learn how to train and evaluate clustering models. Clustering is the process of grouping objects with
similar objects. For example, imagine
a scenario where you have a collection of
two-dimensional coordinates, that have been clustered
into three categories, yellow, red, and blue. A major difference between clustering and
classification models, is that clustering is
an unsupervised method, where training is done
without labels, instead, models identify examples that have a similar
collection of features. For example, images that have a similar color are
grouped together. Clustering is common
and useful for exploring new data where
patterns between data points, such as high level categories
are not yet known. It's used in many
fields that need to automatically label
complex data, including analysis
of social networks, brain connectivity, spam
filtering, and so on.