07181 Introduction – Parallel Universes and Local Patterns

Abstract

Learning in parallel universes and the mining for local patterns
are both relatively new fields of research. Local pattern detection
addresses the problem of identifying (small) deviations from an
overall distribution of some underlying data in some feature space.
Learning in parallel universes on the other hand, deals with the analysis of objects,
which are given in different feature spaces, i.e.\ parallel universes;
and the aim is on finding groups of objects, which show
``interesting'' behavior in some of these universes. So, while
local patterns describe interesting properties of a subset of
the overall space or set of objects, learning in parallel universes
also aims at finding interesting patterns across different feature
spaces or object descriptions. Dagstuhl
Seminar~07181 on Parallel Universes and Local Patterns held in May 2007
brought together researchers with different backgrounds to discuss
latest advances in both fields and to draw connections between the
two.