Can embeddings be used for clustering data?

Yes, embeddings can be effectively used for clustering data. Embeddings are numerical representations of data—like text, images, or categorical variables—that capture semantic or structural relationships in a lower-dimensional space. By converting raw data into dense vectors, embeddings make it easier to measure similarity or dissimilarity between data points, which is a core requirement for clustering algorithms. For example, in natural language processing (NLP), word embeddings like Word2Vec or sentence embeddings from models like BERT map text to vectors where similar meanings correspond to closer points in the vector space. Clustering algorithms like K-means or DBSCAN can then group these vectors into clusters based on their proximity.

A practical example involves clustering customer reviews. Suppose you have thousands of product reviews in raw text. Converting them into embeddings using a model like Sentence-BERT transforms each review into a vector that captures its semantic content. Applying K-means clustering on these vectors groups reviews with similar sentiments or topics (e.g., complaints about shipping, praise for quality). Similarly, in image processing, embeddings from convolutional neural networks (CNNs) can cluster images by visual features—like grouping photos of cars versus bicycles. This approach avoids the need for manual feature engineering and works well with high-dimensional data, which traditional clustering methods struggle to handle directly.

However, the quality of clustering heavily depends on the embedding method and how well it captures relevant features. For instance, using a generic pre-trained embedding model might not work well for domain-specific data (e.g., medical texts) without fine-tuning. Additionally, clustering algorithms require careful parameter selection, such as the number of clusters (K in K-means) or distance thresholds in density-based methods. Tools like UMAP or t-SNE can help visualize embeddings to validate clusters before applying algorithms. While embeddings simplify clustering by reducing noise and dimensionality, developers should still evaluate results with metrics like silhouette scores or domain-specific validation to ensure meaningful groupings.