let x ,..., xn 1 be a random sample from a distribution with sample mean x and samplevariance s 2. in this paper we consider certain very general properties of the so-called “z-scores”x x s i n i ( − )/ : = 1,...., . a representation theorem is then given for z-scores obtained from an underlyingnormal population, together with a theorem for their limiting distribution as the sample size tends t...

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...

In many applications, stream data are too voluminous to be collected in a central fashion and often transmitted on a distributed network. In this paper, we focus on the outlier detection over distributed data streams in real time, firstly, we formalize the problem of outlier detection using the kernel density estimation technique. Then, we adopt the fading strategy to keep pace with the transie...

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...

Outlier Mining has always attract much attention among the data mining community. This paper discusses on the discovery of meaningful outlier detection based on Non_Reduct computation by incorporating the Negative Association Rules. Non_Reduct computation is proposed to detect outliers from rare classes. These outliers may have meaningful knowledge having incorporating the concept of Negatives ...

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 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...

This paper studies a new data mining problem called multiinstance outlier identification. This problem arises in tasks where each sample consists of many alternative feature vectors (instances) that describe it. This paper defines the multi-instance outliers and analyzes the basic types of multiinstance outliers. Two general identification approaches are proposed based on the state-of-the-art (...