Population differentiation can be driven in large part by natural selection, but selectively neutral evolution can play a prominent role in shaping patters of population divergence. The decomposition of the evolutionary history of populations into the relative effects of natural selection and selectively neutral evolution enables an understanding of the causes of population divergence and adapt...

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...

Background: Outlier detection is an important factor in data mining since it is used in various real time applications. Outlier is an extreme points that are not related to any of the class. Dealing with dimensions is the great challenge, due to “curse of dimensionality”, for effective outlier detection. In a high dimensional data space, it is difficult to detect most related points and most un...

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...

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...

The first € price and the £ and $ price are net prices, subject to local VAT. Prices indicated with * include VAT for books; the €(D) includes 7% for Germany, the €(A) includes 10% for Austria. Prices indicated with ** include VAT for electronic products; 19% for Germany, 20% for Austria. All prices exclusive of carriage charges. Prices and other details are subject to change without notice. Al...