How are embeddings used for clustering?

Embeddings are numerical representations of data (like text, images, or user behavior) that capture their semantic or structural features in a high-dimensional vector space. Clustering algorithms group similar data points by measuring distances between these vectors. Since embeddings convert complex data into a format that preserves relationships—like similarity or context—they enable clustering techniques to identify patterns that aren’t obvious in raw data. For example, in natural language processing (NLP), words like “dog” and “puppy” might have embeddings close to each other, allowing clustering algorithms to group them as related concepts even if their raw text forms differ.

The process typically involves three steps. First, data is converted into embeddings using models like Word2Vec for text, ResNet for images, or custom neural networks for domain-specific tasks. For instance, customer reviews could be embedded using BERT to capture their contextual meaning. Next, clustering algorithms like K-means, DBSCAN, or hierarchical clustering are applied to the embeddings. K-means groups vectors into k clusters based on Euclidean distance, while DBSCAN identifies dense regions of points. A practical example is clustering user profiles based on behavioral embeddings (e.g., app usage patterns) to identify distinct user segments. Finally, dimensionality reduction techniques like PCA or UMAP are often used to visualize clusters in 2D/3D, though the actual clustering is done in the original embedding space for accuracy.

Key considerations when using embeddings for clustering include the quality of embeddings and the choice of distance metric. For instance, cosine similarity often works better than Euclidean distance for text embeddings, as it focuses on vector direction rather than magnitude. Hyperparameter tuning (e.g., selecting the number of clusters k in K-means) is critical and can be guided by metrics like silhouette score or domain knowledge. Additionally, embeddings must align with the clustering goal: a model trained on general text might not work for medical document clustering without fine-tuning. Tools like scikit-learn for clustering and Hugging Face Transformers for embedding generation simplify implementation, but testing different combinations of models and algorithms is essential to achieve meaningful results.