Grid-ODF: Detecting Outliers Effectively and Efficiently in Large Multi-dimensional Databases

  • Wei Wang
  • Ji Zhang
  • Hai Wang
Conference paper

DOI: 10.1007/11596448_113

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3801)
Cite this paper as:
Wang W., Zhang J., Wang H. (2005) Grid-ODF: Detecting Outliers Effectively and Efficiently in Large Multi-dimensional Databases. In: Hao Y. et al. (eds) Computational Intelligence and Security. CIS 2005. Lecture Notes in Computer Science, vol 3801. Springer, Berlin, Heidelberg

Abstract

In this paper, we will propose a novel outlier mining algorithm, called Grid-ODF, that takes into account both the local and global perspectives of outliers for effective detection. The notion ofOutlying Degree Factor(ODF), that reflects the factors of both the density and distance, is introduced to rank outliers. A grid structure partitioning the data space is employed to enable Grid-ODF to be implemented efficiently. Experimental results show that Grid-ODF outperforms existing outlier detection algorithms such as LOF and KNN-distance in terms of effectiveness and efficiency.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Wei Wang
    • 1
  • Ji Zhang
    • 2
  • Hai Wang
    • 3
  1. 1.College of Educational ScienceNanjing Normal UniversityChina
  2. 2.Falculty of Computer ScienceDalhousie UniversityHalifaxCanada
  3. 3.Sobey School of BusinessSaint Mary’s UniversityHalifaxCanada

Personalised recommendations