Dynamic graphs are a powerful way to model an evolving set of objects and their ongoing interactions. A broad spectrum of systems, such as information, communication, and social, are naturally represented by dynamic graphs. Outlier (or anomaly) detection in dynamic graphs can provide unique insights into the relationships of objects and identify novel or emerging relationships. To date, outlier...

Outlier detection is usually considered as a pre-processing step for locating in a data set, those objects that do not conform to well-defi ned notions of expected behaviour. It is very important in data mining for discovering novel or rare events, anomalies, vicious actions, exceptional phenomena etc. However, investigation of outlier detection for categorical data sets is especially a challen...

Analyzing exceptional objects is an important mining task. It includes the identification of outliers but also the description of outlier properties in contrast to regular objects. However, existing detection approaches miss to provide important descriptions that allow human understanding of outlier reasons. In this work we present OutRules, a framework for outlier descriptions that enable an e...

In many Knowledge Discovery applications, finding outliers is more interesting than finding inliers in a dataset. The perception of outliers is rare cases in dataset in which is being described as abnormal data in the information table. Outliers detections are applied in many important applications like fraud detection systems to uncover the suspicious objects which may have important knowledge...

Outlier detection has recently become an important problem in many industrial and financial applications. Often, outliers have to be detected from data streams that continuously arrive from data sources. Incremental outlier detection algorithms, aimed at detecting outliers as soon as they appear in a database, have recently become emerging research field. In this paper, we develop an incrementa...

A new procedure for smoothing a gamma-ray burst (GRB) lightcurve and calculating its variability is presented. Applying the procedure to a sample of 25 long GRBs, we have obtained a very tight correlation between the variability and the peak luminosity. The only significant outlier in the sample is GRB 030329. With this outlier excluded, the data scatter is reduced by a factor of ∼ 3 compared t...

A balanced two-factor ANOVA yields a highly significant effect of watering treatment (F = 10.55, df = 1, 24, P = 0.003) and nonsignificant effects of source (F = 0.47, df = 2, 24, P = 0.628) and the watering treatment x source interaction (F = 0.05, df = 2, 24, P = 0.955). There is one low outlier, and one of the treatment groups (Heeia fresh) had substantially less variation than the others, b...