A method for outlier detection based on cluster analysis and visual expert criteria. Issue 5 (3rd November 2019)

• Record Type: Journal Article
• Authors: Lara, Juan A.; Lizcano, David; Rampérez, Víctor; Soriano, Javier
• Other Names: Chang Victor guestEditor.; Aljawarneh Shadi A. guestEditor.; Li Chung‐Sheng guestEditor.
• Is Part Of: Expert systems. Volume 37:Issue 5(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-11-03
• Subjects: clustering -- data mining -- KDD -- outlier detection -- visual expert criteria; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12473
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: Outlier detection is an important problem occurring in a wide range of areas. Outliers are the outcome of fraudulent behaviour, mechanical faults, human error, or simply natural deviations. Many data mining applications perform outlier detection, often as a preliminary step in order to filter out outliers and build more representative models. In this paper, we propose an outlier detection method based on a clustering process. The aim behind the proposal outlined in this paper is to overcome the specificity of many existing outlier detection techniques that fail to take into account the inherent dispersion of domain objects. The outlier detection method is based on four criteria designed to represent how human beings (experts in each domain) visually identify outliers within a set of objects after analysing the clusters. This has an advantage over other clustering‐based outlier detection techniques that are founded on a purely numerical analysis of clusters. Our proposal has been evaluated, with satisfactory results, on data (particularly time series) from two different domains: stabilometry, a branch of medicine studying balance‐related functions in human beings and electroencephalography (EEG), a neurological exploration used to diagnose nervous system disorders. To validate the proposed method, we studied method outlier detection and efficiency in terms of runtime. The results of regression analyses confirm that our proposal is useful for detecting outlier data in different domains, with a false positive rate of less than 2% and a reliability greater than 99%.