_e problem of outlier detection has been widely studied in existing literature because of its numerous applications in fraud detection,medical diagnostics, fault detection, and intrusion detection. A large category of outlier analysis algorithms have been proposed, such as proximity-based methods and local density-basedmethods. _esemethods are effective in ûnding outliers distributed along line...

Investigating the extent (or the existence) of local adaptation is crucial to understanding how populations adapt. When experiments or fitness measurements are difficult or impossible to perform in natural populations, genomic techniques allow us to investigate local adaptation through the comparison of allele frequencies and outlier loci along environmental clines. The thick-billed murre (Uria...

Outlier detection is a data analysis method and has been used to detect and remove anomalous observations from data. In this paper, we firstly introduced some current mainstream outlier detection methodologies, i.e. statistical-based, distance-based, and density-based. Especially, we analyzed distance-based approach and reviewed several kinds of peculiarity factors in detail. Then, we introduce...

We propose an online and local outlier detection technique with low resource consumption based on an unsupervised centered quartersphere support vector machine for wireless sensor networks. Using synthetic data, we demonstrate that our technique achieves better mining performance in terms of parameter selection using difference kernel functions compared to an earlier offline outlier detection t...

In order to solve the defect in the spatial outlier mining algorithm that the spatial objects may be affected by their surrounding abnormal neighbors, a Based K-Nearest Neighbor (BKNN) algorithm was proposed based on the working principle of KNN Graph, which could effectively identify the spatial outliers by using cutting edge strategies. The core idea of BKNN is to calculate the dissimilarity ...

We analyse the interplay of density estimation and outlier detection in density-based outlier detection. By clear and principled decoupling of both steps, we formulate a generalization of density-based outlier detection methods based on kernel density estimation. Embedded in a broader framework for outlier detection, the resulting method can be easily adapted to detect novel types of outliers: ...

For nonrigid image registration, matching the particular structures (or the outliers) that have missing correspondence and/or local large deformations, can be more difficult than matching the common structures with small deformations in the two images. Most existing works depend heavily on the outlier segmentation to remove the outlier effect in the registration. Moreover, these works do not ha...

Our lives are made of social interactions which can be recorded through personal gadgets as well sensors capturing ubiquitous and data. This type data, such spatio-temporal data from the real-time location people, for example, then used inferring translated into behavioural patterns. In this paper, we consider automatic discovery exceptional behaviour interaction focusing on two areas: subgroup...

Accurate and globally referenced global navigation satellite system (GNSS) based vehicular positioning can be achieved in outlier-free open areas. However, the performance of GNSS significantly degraded by outlier measurements, such as multipath effects non-line-of-sight (NLOS) receptions arising from signal reflections buildings. Inspired advantage batch historical data resisting this paper, w...