Fuzzy outlier analysis a combined clustering - Outlier detection approach

Noha Yousri, Mohammed A. Ismail, Mohamed S. Kamel

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

13 Citations (Scopus)

Abstract

Many outlier detection methods identify outliers ignoring any structure in data. However, it is sometimes beneficial to integrate outlierness and a method that groups data, such as clustering. This enhances both outlier and cluster analysis. In this paper, a fuzzy approach is proposed for integrating results from an outlier detection method and a clustering algorithm. A universal set of clusters is proposed which combines clusters obtained from clustering, and a virtual cluster for the outliers. The approach has two phases; the first computes patterns' initial memberships for the outlier cluster, and the second calculates memberships for the universal clusters, using an iterative membership propagation technique. The proposed approach is general and can combine any outlier detection method with any clustering algorithm. Both low and high dimensional data sets are used to illustrate the impact of the proposed approach.

Original languageEnglish
Title of host publication2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007
Pages412-418
Number of pages7
DOIs
Publication statusPublished - 1 Dec 2007
Externally publishedYes
Event2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007 - Montreal, QC, Canada
Duration: 7 Oct 200710 Oct 2007

Other

Other2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007
CountryCanada
CityMontreal, QC
Period7/10/0710/10/07

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ASJC Scopus subject areas

  • Engineering(all)

Cite this

Yousri, N., Ismail, M. A., & Kamel, M. S. (2007). Fuzzy outlier analysis a combined clustering - Outlier detection approach. In 2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007 (pp. 412-418). [4413873] https://doi.org/10.1109/ICSMC.2007.4413873