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Brussels, Belgium Belgium
Dec. 10, 2012 to Dec. 10, 2012
ISBN: 978-1-4673-5164-5
pp: 886-889
Analyzing databases with many attributes per object is a recent challenge. For these high dimensional data it is known that traditional clustering algorithms fail to detect meaningful patterns. As a solution subspace clustering techniques were introduced. They analyze arbitrary subspace projections of 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.
Data mining, Clustering algorithms, Data visualization, Generators, Algorithm design and analysis, Databases, Image color analysis
Stephan Gunnemann, Hardy Kremer, Richard Musiol, Roman Haag, Thomas Seidl, "A Subspace Clustering Extension for the KNIME Data Mining Framework", ICDMW, 2012, 2013 IEEE 13th International Conference on Data Mining Workshops, 2013 IEEE 13th International Conference on Data Mining Workshops 2012, pp. 886-889, doi:10.1109/ICDMW.2012.31
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