- Clustering is an example of unsupervised learning.
- Without any label, those with close distances in the data are classified into clusters.
- It is different from classification, which is supervised learning.
- In other words, it identifies patterns and groups hidden in the data and binds them together.
- Even if there’s label in data, there is a possibility that some data with same label can be grouped into different clusters.
- There are K-Means Clustering, Mean Shift, Gaussian Mixture Model, DBSCAN, Agglomerative Clustering in clustering algorithms and they will be covered in the next post.