Embeddings are a way to represent data points as vectors in a continuous multi-dimensional space. This technique is particularly useful in clustering because it transforms complex data, such as words, images, or documents, into a format that conveys their semantic meaning. When you have data points embedded in a vector space, their spatial proximity indicates similarity; points that are close together in this space are more alike than those that are farther apart. This allows clustering algorithms, like K-means or DBSCAN, to effectively group similar data points based on their embeddings.
For instance, consider a text dataset where each document needs to be clustered by topic. By using techniques such as Word2Vec or sentence embeddings from models like BERT, each document can be transformed into a vector representation capturing its semantic content. Once you have these embeddings, you can apply a clustering algorithm to group the documents. For example, if you choose K-means, you might specify a certain number of clusters, and the algorithm will find clusters of documents that are semantically similar, helping you categorize them into topics such as sports, technology, or health.
Furthermore, embeddings also enable more nuanced clustering. In addition to basic distance measures like Euclidean distance, developers can implement similarity metrics tailored for the specific characteristics of their data. For instance, when dealing with user behaviors in a recommendation system, embeddings can help identify groups of similar user preferences, allowing for targeted recommendations. This versatility makes embeddings a powerful tool in various clustering applications, enhancing insights and enabling better decision-making based on the data analysis.