In the wide-ranging scope of modern statistical data analysis, a key task is identification of outliers. In using an outlier identification procedure, one needs to know its robustness against masking (an “outlier” is undetected) and swamping (a “nonoutlier” is classified as an “outlier”), possibilities which can come about due to the presence of outliers. Study of these issues together is neces...

Contemporaneous outlier blocks (additive or reallocation) caused by special events frequently occur in repeated business time series. When the time series have strong inter-series dependence, shrinkage estimation techniques provide improved estimates of the time series model parameters and of the outlier block. A bootstrap estimate of the covariance matrix of the vector of outlier magnitudes en...

In many computer vision applications for recognition or classification, outlier detection plays an important role as it affects the accuracy and reliability of the result. We propose a novel approach for outlier detection using Gaussian process classification. With this approach, the outlier detection can be integrated to the classification process, instead of being treated separately. Experime...

Outlier detection has been a very important concept in the realm of data analysis. Recently, several application domains have realized the direct mapping between outliers in data and real world anomalies, that are of great interest to an analyst. Outlier detection has been researched within various application domains and knowledge disciplines. This survey provides a comprehensive overview of e...

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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 (...

For many KDD applications finding the outliers, i.e. the rare events, is more interesting and useful than finding the common cases, e.g. detecting criminal activities in E-commerce. Being an outlier, however, is not just a binary property. Instead, it is a property that applies to a certain degree to each object in a data set, depending on how ‘isolated’ this object is, with respect to the surr...

The purpose of this paper is to identify the effective points on the performance of one of the important algorithm of data mining namely support vector machine. The final classification decision has been made based on the small portion of data called support vectors. So, existence of the atypical observations in the aforementioned points, will result in deviation from the correct decision. Thus...

The term “outlier" can generally be defined as an observation that is significantly different from the other values in a data set. The outliers may be instances of error or indicate events. The task of outlier detection aims at identifying such outliers in order to improve the analysis of data and further discover interesting and useful knowledge about unusual events within numerous application...