  Analyzing high dimensional data is a challenging task. For these data it is
known that traditional clustering algorithms fail to detect meaningful
patterns. As a solution, subspace clustering techniques have been introduced.
They analyze arbitrary subspace projections of the data to detect clustering
structures.
  In this paper, we present our subspace clustering extension for KDD
frameworks, termed KDD-SC. In contrast to existing subspace clustering
toolkits, our solution neither is a standalone product nor is it tightly
coupled to a specific KDD framework. Our extension is realized by a common
codebase and easy-to-use plugins for three of the most popular KDD frameworks,
namely KNIME, RapidMiner, and WEKA. KDD-SC extends these frameworks such that
they offer a wide range of different subspace clustering functionalities. It
provides a multitude of algorithms, data generators, evaluation measures, and
visualization techniques specifically designed for subspace clustering. These
functionalities integrate seamlessly with the frameworks' existing features
such that they can be flexibly combined. KDD-SC is publicly available on our
website.
