The aim of this paper is to test the hypothesis that software piracy rats are lower in more intelligent nations. Thus, we econometrically estimate the effect of national IQ on software piracy rates, using data for 102 nations for the year 2011. Our findings offer strong support for the assertion that intelligence is inversely related to the software piracy rates. After controlling for the poten...

Data objects which differ significantly from the remaining data objects are referred to as outliers. Density-based algorithms for mining outliers are very effective in detecting all forms of outliers, where data objects with fewer neighbors are likely to be outliers than are those with more neighbors. However, existing density-based algorithms engage in huge repetitive computation and compariso...

Successfully detecting outliers in multivariate data requires statistical and programming skills and can be very time consuming. Requests for outlier detection can come from different skills groups therefore it is more efficient and effective to allow users to interact directly with the data themselves. We have developed an interactive, web based data visualization application for outlier detec...

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...