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Analyzing high dimensional data is a challenging task. For these data it isknown that traditional clustering algorithms fail to detect meaningfulpatterns. As a solution, subspace clustering techniques have been introduced.They analyze arbitrary subspace projections of the data to detect clusteringstructures.  In this paper, we present our subspace clustering extension for KDDframeworks, termed KDD-SC. In contrast to existing subspace clusteringtoolkits, our solution neither is a standalone product nor is it tightlycoupled to a specific KDD framework. Our extension is realized by a commoncodebase and easy-to-use plugins for three of the most popular KDD frameworks,namely KNIME, RapidMiner, and WEKA. KDD-SC extends these frameworks such thatthey offer a wide range of different subspace clustering functionalities. Itprovides a multitude of algorithms, data generators, evaluation measures, andvisualization techniques specifically designed for subspace clustering. Thesefunctionalities integrate seamlessly with the frameworks' existing featuressuch that they can be flexibly combined. KDD-SC is publicly available on ourwebsite.

KDD-SC: Subspace Clustering Extensions for Knowledge Discovery Frameworks