Reliably Capture Local Clusters in Noisy Domains From Parallel Universes
Frank Höppner, Mirko Böttcher · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · 2007
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.