  Information distance is a parameter-free similarity measure based on
compression, used in pattern recognition, data mining, phylogeny, clustering,
and classification. The notion of information distance is extended from pairs
to multiples (finite lists). We study maximal overlap, metricity, universality,
minimal overlap, additivity, and normalized information distance in multiples.
We use the theoretical notion of Kolmogorov complexity which for practical
purposes is approximated by the length of the compressed version of the file
involved, using a real-world compression program.
  {\em Index Terms}-- Information distance, multiples, pattern recognition,
data mining, similarity, Kolmogorov complexity
