A new method for clustering heterogeneous data: clustering by compression
Nowadays, we have to deal with a large quantity of unstructured data, produced by a number of sources. For example, clustering web pages is essential to getting structured information in response to user queries. In this paper, we intend to test the results of a new clustering technique clustering by compression when applied to heterogeneous sets of data. The clustering by compression procedure is based on a parameterfree, universal, similarity distance, the normalized compression distance or NCD, computed from the lengths of compressed data files (singly and in pair wise concatenation) Compression algorithms allow defining a similarity measure based on the degree of common information, whereas clustering methods allow clustering similar data without any previous knowledge.