As mentioned in Chapter 1, Getting Started with Machine Learning and ML.NET, k-means clustering, by definition, is an unsupervised learning algorithm. This means that data is grouped into clusters based on the data provided to the model for training. In this section, we will dive into a number of use cases for clustering and the k-means trainer.
Use cases for clustering
Clustering, as you may be beginning to realize, has numerous applications where the output categorizes similar outputs into groups of similar data points.
Some of its potential applications include the following:
- Natural disaster tracking such as earthquakes or hurricanes and creating clusters of high-danger zones
- Book or document grouping based on the authors, subject matter, and sources
- Grouping customer data into targeted marketing predictions
- Search result grouping of similar results that other users found useful
In addition, it has numerous other applications such as predicting...
