A Subspace Clustering Extension for the KNIME Data Mining Framework

Analyzing databases with many attributes per object is a recent challenge. For these highdimensional data it is known that traditional clustering algorithmsfail to detect meaningful patterns. As a solution subspace clustering techniqueswere introduced. They analyze arbitrary subspace projectionsof the data to detect clustering structures.


In this demonstration, we introduce the first subspace clustering extension for the well-established KNIME data mining framework. While KNIME offers a variety of data mining functionalities, subspace clustering is missing so far. Our novel extension provides a multitude of algorithms, data generators, evaluation measures, and visualization techniques specifically designed for subspace clustering.It deeply integrates into the KNIME framework allowing a flexible combination of the existing KNIME features with the novel subspace components.The extension is available on our website.


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Authors: Günnemann S., Kremer H., Musiol R., Haag R., Seidl T.
Published in: Proc. IEEE International Conference on Data Mining Workshops (ICDMW), Brussels, Belgium
Publisher: IEEE Computer Society - Washington,USA
Sprache: EN
Jahr: 2012


Seiten: 886-889
ISBN: 978-1-4673-5164-5
Konferenz: ICDM
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Typ: Tagungsbeiträge
Forschungsgebiet: Data Analysis and Knowledge Extraction