This study uses a comparative genome scan to evaluate the contributions of host plant related divergent selection to genetic differentiation and ecological speciation in maple- and willow-associated populations of Neochlamisus bebbianae leaf beetles. For each of 15 pairwise population comparisons, we identified "outlier loci" whose strong differentiation putatively reflects divergent selection....

Many real applications are required to detect outliers in high dimensional data sets. The major difficulty of mining outliers lies on the fact that outliers are often embedded in subspaces. No efficient methods are available in general for subspace-based outlier detection. Most existing subspacebased outlier detection methods identify outliers by searching for abnormal sparse density units in s...

Five procedures for detecting outliers in linear regression are compared: sequential testing of the maximum studentized residual, Marasinghe's (1985) multistage procedure, and three procedures based on recursive residuals, calculated on adaptively-ordered observations. Four of these procedures test a no-outliers hypothesis. This hypothesis requires a rather stringent test criterion, meaning tha...

The detection of outliers has gained considerable interest in data mining with the realization that outliers can be the key discovery to be made from very large databases. Outliers arise due to various reasons such as mechanical faults, changes in system behavior, fraudulent behavior, human error and instrument error. Indeed, for many applications the discovery of outliers leads to more interes...

A spatial outlier is a spatially referenced object whose non-spatial attribute values are significantly different from the values of its neighborhood. Identification of spatial outliers can lead to the discovery of unexpected, interesting, and useful spatial patterns for further analysis. One drawback of existing methods is that normal objects tend to be falsely detected as spatial outliers whe...