Yes, embeddings can be used for clustering data by representing data points as vectors in a continuous space. Once the data points are transformed into embeddings, clustering algorithms like k-means or hierarchical clustering can be applied to group similar data points together. The advantage of using embeddings is that they capture the underlying structure and relationships within the data, enabling more meaningful clustering results.
For instance, in text clustering, embeddings such as word or sentence embeddings are generated for each document, and then clustering algorithms can group documents that are semantically similar. In image clustering, embeddings representing visual features can be used to cluster images with similar content, such as grouping photos of cats or dogs together. Embeddings allow for clustering of data from diverse sources, including text, images, or audio, making them highly versatile.
The effectiveness of embeddings in clustering tasks lies in their ability to reduce the dimensionality of the data while preserving important relationships. This leads to more accurate and interpretable clusters, particularly in large datasets where traditional clustering techniques may struggle. Embeddings are widely used in customer segmentation, content categorization, and anomaly detection, where the goal is to group similar items or identify outliers.