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Outlier detection and ranking based on subspace clustering Seidl, Thomas Müller, Emmanuel Assent, Ira Steinhausen, Uwe Outlier detection outlier ranking subspace clustering data mining Detecting outliers is an important task for many applications including fraud detection or consistency validation in real world data. Particularly in the presence of uncertain data or imprecise data, similar objects regularly deviate in their attribute values. The notion of outliers has thus to be defined carefully. When considering outlier detection as a task which is complementary to clustering, binary decisions whether an object is regarded to be an outlier or not seem to be near at hand. For high-dimensional data, however, objects may belong to different clusters in different subspaces. More fine-grained concepts to define outliers are therefore demanded. By our new OutRank approach, we address outlier detection in heterogeneous high dimensional data and propose a novel scoring function that provides a consistent model for ranking outliers in the presence of different attribute types. Preliminary experiments demonstrate the potential for successful detection and reasonable ranking of outliers in high dimensional data sets. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Thomas Seidl and Emmanuel Müller and Ira Assent and Uwe Steinhausen 2009 Is Part Of Dagstuhl Seminar Proceedings, Volume 8421, Uncertainty Management in Information Systems (2009) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/DagSemProc.08421.10 urn:nbn:de:0030-drops-19344 https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08421.10 eng https://creativecommons.org/licenses/by/4.0/legalcode