Abstract
The skyline operator is a well established database primitive which is traditionally applied in a way that only a single skyline is computed. In this paper we use multiple skylines themselves as objects for data exploration and data mining. We define a novel similarity measure for comparing different skylines, called SkyDist. SkyDist can be used for complex analysis tasks such as clustering, classification, outlier detection, etc. We propose two different algorithms for computing SkyDist, based on Monte-Carlo sampling and on the plane sweep paradigm. In an extensive experimental evaluation, we demonstrate the efficiency and usefulness of SkyDist for a number of applications and data mining methods.
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Böhm, C., Oswald, A., Plant, C., Plavinski, M., Wackersreuther, B. (2010). SkyDist: Data Mining on Skyline Objects. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2010. Lecture Notes in Computer Science(), vol 6118. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13657-3_49
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DOI: https://doi.org/10.1007/978-3-642-13657-3_49
Publisher Name: Springer, Berlin, Heidelberg
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