What is Unsupervised Clustering?

Unsupervised clustering is a machine-learning method that does not require labelled instances in order to find hidden patterns or groupings within data. It entails dividing data points according to distance or similarity measures into discrete clusters.

There are several types of unsupervised clustering algorithms, each with its approach to grouping data points. Some of the most common types include:

1. K-Means Clustering: A partitioning algorithm that divides data points into k clusters based on their features, with each cluster represented by the mean of its data points.
2. Hierarchical Clustering: Builds a hierarchy of clusters either from the bottom up (agglomerative) or from the top down (divisive), where each data point starts in its cluster and pairs of clusters are merged or split recursively.
3. DBSCAN (Density-Based Spatial Clustering of Applications with Noise): Clusters together closely packed points and marks points that are in low-density regions as outliers. It can find clusters of varying shapes and sizes.

The aim of unsupervised clustering, a fundamental machine learning problem, is to divide data into groups or clusters based on resemblance or some underlying structure. One well-liked deep learning framework for unsupervised clustering problems is PyTorch.