There is a considerable impetus in population genomics to pinpoint loci involved in local adaptation. A powerful approach to find genomic regions subject to local adaptation is to genotype numerous molecular markers and look for outlier loci. One of the most common approaches for selection scans is based on statistics that measure population differentiation such as FST. However, there are impor...

Motion segmentation, which is a crucial technology in reuse of motion capture data, means automatically dividing a long motion sequence into several motion clips which have different semantics. In this paper, a new motion segmentation method based on Local Outlier Factor (LOF) is proposed. Our method is based on the assumption that motions with same type can form a cluster and the transition of...

Efficient outlier detection in a large-sized big data environment incurs much of complexity in processing the information and to handle it in a proficient way. For segregating outliers from those normal data items, many of the prevailing methodologies experiences complexity in accordance with the features involved in every single attribute. On recognizing appropriate features associated the cha...

The need for technology has always found space in Financial Transaction as the number of fraud financial transactions increases day by day. In this research we have proposed a new methodology using isolation forest algorithm and local outlier detection to detect fraud. A standard data set is used experimentation classify transaction occurred fraudulent or not. We neural networks machine learnin...

Outlier detection (OD) is widely used in many fields, such as finance, information and medicine, in cleaning up datasets and keeping the useful information. In a traffic system, it alerts the transport department and drivers with abnormal traffic situations such as congestion and traffic accident. This paper presents a density-based bounded LOF (BLOF) method for large-scale traffic video data i...