Algorithms for detecting outliers via clustering and ranks

Huaming Huang, Kishan Mehrotra, Chilukuri K. Mohan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

Rank-based algorithms provide a promising approach for outlier detection, but currently used rank-based measures of outlier detection suffer from two deficiencies: first they assign a large value to an object near a cluster whose density is high even through the object may not be an outlier and second the distance between the object and its nearest cluster plays a mild role though its rank with respect to its neighbor. To correct for these deficiencies we introduce the concept of modified-rank and propose new algorithms for outlier detection based on this concept. Our method performs better than several density-based methods, on some synthetic data sets as well as on some real data sets.

Original languageEnglish (US)
Title of host publicationAdvanced Research in Applied Artificial Intelligence - 25th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2012, Proceedings
Pages20-29
Number of pages10
DOIs
StatePublished - Aug 1 2012
Event25th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2012 - Dalian, China
Duration: Jun 9 2012Jun 12 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7345 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other25th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2012
CountryChina
CityDalian
Period6/9/126/12/12

Keywords

  • Outlier detection
  • clustering
  • neighborhood sets
  • ranking

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Huang, H., Mehrotra, K., & Mohan, C. K. (2012). Algorithms for detecting outliers via clustering and ranks. In Advanced Research in Applied Artificial Intelligence - 25th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2012, Proceedings (pp. 20-29). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7345 LNAI). https://doi.org/10.1007/978-3-642-31087-4_3