Outlier Ranking via Subspace Analysis in Multiple Views of the Data

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

  • Emmanuel Muller, Karlsruhe Intitute of Technology; University of Antwerp
  • ,
  • Ira Assent
  • Patricia Iglesias, Karlsruhe Institute of Technology, Germany
  • Yvonne Mülle, Karlsruhe Institute of Technology
  • ,
  • Klemens Böhm Böhm, Karlsruhe Institute of Technology, Germany

Outlier mining is an important task for finding anomalous objects. In practice, however, there is not always a clear distinction between outliers and regular objects as objects have different roles w.r.t. different attribute sets. An object may deviate in one subspace, i.e. a subset of attributes. And the same object might appear perfectly regular in other subspaces. One can think of subspaces as multiple views on one database. Traditional methods consider only one view (the full attribute space). Thus, they miss complex outliers that are hidden in multiple subspaces. In this work, we propose Outrank, a novel outlier ranking concept. Outrank exploits subspace analysis to determine the degree of outlierness. It considers different subsets of the attributes as individual outlier properties. It compares clustered regions in arbitrary subspaces and derives an outlierness score for each object. Its principled integration of multiple views into an outlierness measure uncovers outliers that are not detectable in the full attribute space. Our experimental evaluation demonstrates that Outrank successfully determines a high quality outlier ranking, and outperforms state-of-the-art outlierness measures.
Original languageEnglish
Title of host publicationProceedings of the 12th IEEE International Conference on Data Mining, ICDM
Number of pages10
PublisherIEEE Communications Society
Publication year2012
Pages529 - 538
ISBN (print)978-1-4673-4649-8
Publication statusPublished - 2012
EventIEEE International Conference on Data Mining - Brussels, Belgium
Duration: 10 Dec 201213 Feb 2013
Conference number: ICDM


ConferenceIEEE International Conference on Data Mining

    Research areas

  • clusterings , multiple subspaces , outlier ranking

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