An extension of the PMML standard to subspace clustering models
Stephan Günnemann, Hardy Kremer, Thomas Seidl · 2011
In today's applications we face the challenge of analyzing databases with many attributes per object. For these high dimensional data it is known that traditional clustering algorithms fail to detect meaningful patterns: mining the full-space is futile. As a solution subspace clustering techniques were introduced. They analyze arbitrary subspace projections of the data to detect clustering structures. Recently, public available mining software integrates subspace clustering as a novel mining paradigm and sets the stage for its wide applicability. Though, a common standard to describe, exchange and process the subspace clustering results is still missing, which hinders the application in practice.