Study and implementation conditions of the multivariate outlier detection methods for screening of potential field failures

2015 ◽  
Author(s):  
Corinne Berges ◽  
Chunlei Wu ◽  
Pierre Soufflet
Keyword(s):  
Outlier Detection ◽  
Potential Field ◽  
Detection Methods ◽  
Multivariate Outlier Detection

Robust Multivariate Outlier Detection Methods for Environmental Data

2010 ◽  
Vol 136 (11) ◽  
pp. 1299-1304 ◽  
Author(s):  
Ibrahim Alameddine ◽  
Melissa A. Kenney ◽  
Russell J. Gosnell ◽  
Kenneth H. Reckhow
Keyword(s):  
Outlier Detection ◽  
Environmental Data ◽  
Detection Methods ◽  
Multivariate Outlier Detection

scholarly journals Multivariate Outlier Detection in Postprocessing of Multi-temporal PS-InSAR Results using Deep Learning

2021 ◽  
Vol 181 ◽  
pp. 1146-1153
Author(s):  
Pedro Aguiar ◽  
António Cunha ◽  
Matus Bakon ◽  
Antonio M. Ruiz-Armenteros ◽  
Joaquim J. Sousa
Keyword(s):  
Deep Learning ◽  
Outlier Detection ◽  
Multivariate Outlier Detection ◽  
Multi Temporal

scholarly journals Benchmarking Unsupervised Outlier Detection with Realistic Synthetic Data

2021 ◽  
Vol 15 (4) ◽  
pp. 1-20
Author(s):  
Georg Steinbuss ◽  
Klemens Böhm
Keyword(s):  
Outlier Detection ◽  
Synthetic Data ◽  
Real Data ◽  
Detection Methods ◽  
Quality Of Data ◽  
Benchmark Data ◽  
Core Idea ◽  
Generic Process ◽  
Unsupervised Outlier Detection

Benchmarking unsupervised outlier detection is difficult. Outliers are rare, and existing benchmark data contains outliers with various and unknown characteristics. Fully synthetic data usually consists of outliers and regular instances with clear characteristics and thus allows for a more meaningful evaluation of detection methods in principle. Nonetheless, there have only been few attempts to include synthetic data in benchmarks for outlier detection. This might be due to the imprecise notion of outliers or to the difficulty to arrive at a good coverage of different domains with synthetic data. In this work, we propose a generic process for the generation of datasets for such benchmarking. The core idea is to reconstruct regular instances from existing real-world benchmark data while generating outliers so that they exhibit insightful characteristics. We propose and describe a generic process for the benchmarking of unsupervised outlier detection, as sketched so far. We then describe three instantiations of this generic process that generate outliers with specific characteristics, like local outliers. To validate our process, we perform a benchmark with state-of-the-art detection methods and carry out experiments to study the quality of data reconstructed in this way. Next to showcasing the workflow, this confirms the usefulness of our proposed process. In particular, our process yields regular instances close to the ones from real data. Summing up, we propose and validate a new and practical process for the benchmarking of unsupervised outlier detection.

A New Procedure of Clustering Based on Multivariate Outlier Detection

2021 ◽  
Vol 11 (1) ◽  
pp. 69-84
Author(s):  
G. S. David Sam Jayakumar ◽  
Bejoy John Thomas
Keyword(s):  
Outlier Detection ◽  
Multivariate Outlier Detection

Outlier Detection Techniques for Data Mining

2011 ◽  
pp. 1483-1488
Author(s):  
Fabrizio Angiulli
Keyword(s):  
Data Mining ◽  
Outlier Detection ◽  
Credit Card ◽  
Detection Methods ◽  
Distribution Model ◽  
Main Task ◽  
Data Set ◽  
Homogeneous Groups ◽  
Definition Of ◽  
Dependency Detection

Data mining techniques can be grouped in four main categories: clustering, classification, dependency detection, and outlier detection. Clustering is the process of partitioning a set of objects into homogeneous groups, or clusters. Classification is the task of assigning objects to one of several predefined categories. Dependency detection searches for pairs of attribute sets which exhibit some degree of correlation in the data set at hand. The outlier detection task can be defined as follows: “Given a set of data points or objects, find the objects that are considerably dissimilar, exceptional or inconsistent with respect to the remaining data”. These exceptional objects as also referred to as outliers. Most of the early methods for outlier identification have been developed in the field of statistics (Hawkins, 1980; Barnett & Lewis, 1994). Hawkins’ definition of outlier clarifies the approach: “An outlier is an observation that deviates so much from other observations as to arouse suspicions that it was generated by a different mechanism”. Indeed, statistical techniques assume that the given data set has a distribution model. Outliers are those points that satisfy a discordancy test, that is, that are significantly far from what would be their expected position given the hypothesized distribution. Many clustering, classification and dependency detection methods produce outliers as a by-product of their main task. For example, in classification, mislabeled objects are considered outliers and thus they are removed from the training set to improve the accuracy of the resulting classifier, while in clustering, objects that do not strongly belong to any cluster are considered outliers. Nevertheless, it must be said that searching for outliers through techniques specifically designed for tasks different from outlier detection could not be advantageous. As an example, clusters can be distorted by outliers and, thus, the quality of the outliers returned is affected by their presence. Moreover, other than returning a solution of higher quality, outlier detection algorithms can be vastly more efficient than non ad-hoc algorithms. While in many contexts outliers are considered as noise that must be eliminated, as pointed out elsewhere, “one person’s noise could be another person’s signal”, and thus outliers themselves can be of great interest. Outlier mining is used in telecom or credit card frauds to detect the atypical usage of telecom services or credit cards, in intrusion detection for detecting unauthorized accesses, in medical analysis to test abnormal reactions to new medical therapies, in marketing and customer segmentations to identify customers spending much more or much less than average customer, in surveillance systems, in data cleaning, and in many other fields.

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