We present a method to find outliers using ‘commute distance’ computed from a random walk on graph. Unlike Euclidean distance, commute distance between two nodes captures both the distance between them and their local neighborhood densities. Indeed commute distance is the Euclidean distance in the space spanned by eigenvectors of the graph Laplacian matrix. We show by analysis and experiments t...

Smooth local coordinates have been proposed by Hiyoshi and Sugihara 2000 to improve the classical Sibson’s and Laplace coordinates. These smooth local coordinates are computed by integrating geometric quantities over weights in the power diagram. In this paper we describe how to efficiently implement the Voronoi based C local coordinates. The globally C interpolant that Hiyoshi and Sugihara pre...

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...

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...

Occurrences of outliers in multivariate time series are unpredictable events which may severely distort the analysis of the series. It may be noticed that a convenient way for representing multiple outliers consists in superimposing a deterministic disturbance to a Gaussian multivariate time series. Then outliers may be modelled as non – Gaussian time series components. The independent componen...