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Reliably Capture Local Clusters in Noisy Domains From Parallel Universes

Authors Frank Höppner, Mirko Böttcher

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DagSemProc.07181.9.pdf
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Keywords
  • Local pattern
  • time
  • parallel universe

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Abstract

When seeking for small local patterns it is very intricate to
distinguish between incidental agglomeration of noisy points and true
local patterns. We propose a new approach that
addresses this problem by exploiting temporal information which is
contained in most business data sets. The algorithm enables the
detection of local patterns in noisy data sets more reliable compared
to the case when the temporal information is ignored. This is achieved
by making use of the fact that noise does not reproduce its incidental
structure but even small patterns do. In particular, we developed a
method to track clusters over time based on an optimal match of data
partitions between time periods.

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Frank Höppner and Mirko Böttcher. Reliably Capture Local Clusters in Noisy Domains From Parallel Universes. In Parallel Universes and Local Patterns. Dagstuhl Seminar Proceedings, Volume 7181, pp. 1-2, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007) https://doi.org/10.4230/DagSemProc.07181.9

Author Details

Frank Höppner
Mirko Böttcher
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