OutRank: ranking outliers in high dimensional data

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

  • Emmanuel Müller, RWTH Aachen University, Germany
  • Ira Assent
  • Uwe Steinhausen, RWTH Aachen University, Germany
  • Thomas Seidl, RWTH Aachen University, Germany
Outlier detection is an important data mining task for consistency checks, fraud detection, etc. Binary decision making on whether or not an object is an outlier is not appropriate in many applications and moreover hard to parametrize. Thus, recently, methods for outlier ranking have been proposed. Determining the degree of deviation, they do not require setting a decision boundary between outliers and the remaining data. High dimensional and heterogeneous (continuous and categorical attributes) data, however, pose a problem for most outlier ranking algorithms. In this work, we propose our OutRank approach for ranking outliers in heterogeneous high dimensional data. We introduce a consistent model for different attribute types. Our novel scoring functions transform the analyzed structure of the data to a meaningful ranking. Promising results in preliminary experiments show the potential for successful outlier ranking in high dimensional data.
Original languageEnglish
Title of host publicationIEEE 24th International Conference on Data Engineering Workshop, 2008. ICDEW 2008
Publication year2008
Pages600-603
ISBN (print)978-1-4244-2161-9
ISBN (Electronic)978-1-4244-2161-9
DOIs
Publication statusPublished - 2008
Externally publishedYes

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