Clustering heterogeneous data using clustering by compression
Alexandra Suzana Cernian, Dorin Cârstoiu · Annual Conference on Computers · 2009
Nowadays, we have to deal with a large quantity of unstructured data, produced by a number of sources. The application of clustering on the World Wide Web 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 parameter-free, universal, similarity distance, the normalized compression distance or NCD, computed from the lengths of compressed data files (singly and in pair-wise concatenation).