Applying Compression to Hierarchical Clustering

Hierarchical Clustering is widely used in Machine Learning and Data Mining. It stores bit-vectors in the nodes of a k-ary tree, usually without trying to compress them. We suggest a data compression application of hierarchical clustering with a double usage of the xoring operations defining the Hamming distance used in the clustering process, extending it also to be used to transform the vector in one node into a more compressible form, as a function of the vector in the parent node. Compression is then achieved by run-length encoding, followed by optional Huffman coding, and we show how the compressed file may be processed directly, without decompression.