Compression-based methods transform data points to an embedding and subsequently perform clustering on the lower-dimension data.

Examples

• Spectral clustering
  • transforms the affinity matrix between data points to a low-dimension embedding before performing clustering (such as Centroid-based Clustering#K-Means Clustering)
• BIRCH
  • BIRCH performs lossy compression of data points to a set of Clustering Features nodes (CF Nodes) that forms the Clustering Feature Tree (CFT). New data points are ‘shuffled’ into the tree until it reaches a leaf and store information regarding the subcluster within the node.
  • Information regarding the final cluster centroids is read off the leaf and other clustering algorithms (such as Connectivity-based Clustering#Hierarchical Clustering) can be subsequently performed.