Based on the idea of Weighted Spatial Outlier (WSO), this study identifies the influences of spatial attributes on the calculation of spatial outlying degree, combines these influences with non-spatial attributes, and proposes two revised spatial outlier detection algorithms, Improved Z-value (IZ-value) algorithm and Weighted Difference Algorithm (WDA). The proposed algorithms are detailed in t...

Instant identification of outlier patterns is very important in modern-day engineering problems such as credit card fraud detection and network intrusion detection. Most previous studies focused on finding outliers that are hidden in numerical datasets. Unfortunately, those outlier detection methods were not directly applicable to real life transaction databases. Outlier detection methods are d...

Now day’s Outlier Detection is used in various fields such as Credit Card Fraud Detection, Cyber-Intrusion Detection, Medical Anomaly Detection, and Data Mining etc. So to detect anomaly objects from various types of dataset Outlier Detection techniques are used, that detects and remove the anomaly objects from the dataset. Outliers are the containments that divert from the other objects. Outli...

Data association is an important data-mining task and it has various applications. In crime analysis, data association means to link criminal incidents committed by the same person. It helps to discover crime patterns and catch the criminal. In this paper, we present an outlier-based data association method. An outlier score function is defined to measure the extremeness of an observation, and ...

In this paper the task of identifying outliers in exponential samples is treated conceptionally in the sense of Davies and Gather by means of a so called outlier region In case of an exponential distribution an empirical approximation of such a region also called an outlier identi er is mainly dependent on some estimator of the unknown scale parameter The worst case behaviour of several reasona...

Outlier detection amounts to finding data points that differ significantly from the norm. Classic outlier detection methods are largely designed for single data type such as continuous or discrete. However, real world data is increasingly heterogeneous, where a data point can have both discrete and continuous attributes. Handling mixed-type data in a disciplined way remains a great challenge. I...

Eelgrass Zostera marina is an ecosystem-engineering species of outstanding importance for coastal soft sediment habitats that lives in widely diverging habitats. Our Wrst goal was to detect divergent selection and habitat adaptation at the molecular genetic level; hence, we compared three pairs of permanently submerged versus intertidal populations using genome scans, a genetic markerbased appr...

Automatic approaches to creating and extending wordnets, which have become very popular in the past decade, inadvertently result in noisy synsets. This is why we propose an approach to detect synset outliers in order to eliminate the noise and improve accuracy of the developed wordnets, so that they become more useful lexico-semantic resources for natural language applications. The approach com...

This paper studies the outlier detection problem from the point of view of penalized regressions. Our regression model adds one mean shift parameter for each of the n data points. We then apply a regularization favoring a sparse vector of mean shift parameters. The usual L1 penalty yields a convex criterion, but we find that it fails to deliver a robust estimator. The L1 penalty corresponds to ...

Outlier detection in laser scanner point clouds is an essential process before the modelling step. However, the number of points in the generated point cloud is in the order of million points, so (semi) automatic approaches are necessary. Having introduced the sources of outliers in typical laser scanner point clouds, an outlier detection algorithm using a density based algorithm is addressed. ...