We propose an inlier-based outlier detection method capable of both identifying the outliers and explaining why they are outliers, by identifying the outlier-specific features. Specifically, we employ an inlier-based outlier detection criterion, which uses the ratio of inlier and test probability densities as a measure of plausibility of being an outlier. For estimating the density ratio functi...

Outlier detection has recently become an important problem in many data mining applications. In this paper, a novel unsupervised algorithm for outlier detection is proposed. First we apply a provably globally optimal Expectation Maximization (EM) algorithm to fit a Gaussian Mixture Model (GMM) to a given data set. In our approach, a Gaussian is centered at each data point, and hence, the estima...

Outlier detection methods have used approximate neighborhoods in filter-refinement approaches. Outlier detection ensembles have used artificially obfuscated neighborhoods to achieve diverse ensemble members. Here we argue that outlier detection models could be based on approximate neighborhoods in the first place, thus gaining in both efficiency and effectiveness. It depends, however, on the ty...

Outlier genes with marked overexpression in subsets of cancers like ERBB2 have potential for the identification of gene classifiers and therapeutic targets for the appropriate subpopulation. In this study, using the cancer outlier profile analysis strategy, we identified WNT1-inducible-signaling pathway protein 3 (WISP3) as an outlier gene that is highly expressed in a subset of colorectal canc...

The task of outlier detection is to find the small groups of data objects that are exceptional to the inherent behavior of the rest of the data. Detection of such outliers is fundamental to a variety of database and analytic tasks such as fraud detection and customer migration. There are several approaches[10] of outlier detection employed in many study areas amongst which distance based and de...

In high dimensional data large no of outliers are embedded in low dimensional subspaces known as projected outliers, but most of existing outlier detection techniques are unable to find these projected outliers, because these methods perform detection of abnormal patterns in full data space. So, outlier detection in high dimensional data becomes an important research problem. In this paper we a...

Outliers detection is a task that finds objects that are dissimilar or inconsistent with respect to the remaining data. It has many uses in applications like fraud detection, network intrusion detection and clinical diagnosis of diseases. Using clustering algorithms for outlier detection is a technique that is frequently used. The clustering algorithms consider outlier detection only to the poi...

A class-modular generalized learning vector quantization (GLVQ) ensemble method with outlier learning for handwritten digit recognition is proposed. A GLVQ classifier is one of discriminative methods. Though discriminative classifiers have remarkable ability to solve character recognition problems, they are poor at outlier resistance. To overcome this problem, a GLVQ classifier trained with bot...

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: ...

Outlier detection is a data mining method for discovering exceptional, abnormal or suspiciously unusual samples in a data set. Outliers typically represent the data rich but information poor dilemma. Data mining methods are applied to solve this problem in broad range of application fields like credit card fraud detection, network intrusion detection, error extraction, clinical disease research...