In many applications, stream data are too voluminous to be collected in a central fashion and often transmitted on a distributed network. In this paper, we focus on the outlier detection over distributed data streams in real time, firstly, we formalize the problem of outlier detection using the kernel density estimation technique. Then, we adopt the fading strategy to keep pace with the transie...

Outliers are the points which are different from or inconsistent with the rest of the data. They can be novel, new, abnormal, unusual or noisy information. Outliers are sometimes more interesting than the majority of the data. The main challenges of outlier detection with the increasing complexity, size and variety of datasets, are how to catch similar outliers as a group, and how to evaluate t...

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...

Although alarms in plants are designed to notify any anomaly or faults in order to prevent accidents or to improve process, it is very difficult for the operators to identify meaningful alarms, since there are large volumes of false and nuisance alarms. Outlier detection algorithms are used to identify anomaly in data, and thus they can be used to suggest abnormal alarms. In this research, we a...

Outlier is defined as an observation that deviates too much from other observations. The identification of outliers can lead to the discovery of useful and meaningful knowledge. Outlier detection has been extensively studied in the past decades. However, most existing research focuses on the algorithm based on special background, compared with outlier detection approach is still rare. Most soph...

Outliers are the data objects that clearly differ in their behavior from the normal data. Outlier detection mainly aims at finding these data objects. Outlier detection has become the major area of research in data mining. This plays a crucial role in data mining. Most of the methods used for outlier detection, consider the positive data and their behavior, and then the data violating the behav...

Outlier detection, also known as anomaly detection, is an important topic that has been studied for decades. An outlier detection system is able to identify anomalies in a dataset and thus improve data integrity by removing the detected outliers. It has been successfully applied to different types of data in various fields such as cyber-security, finance, and transportation. In the field of Mus...

This paper studies the problem of top-k distance-based outlier detection on uncertain data. In this work, an uncertain object is modelled by a probability density function of a Gaussian distribution. We start with the Naive approach. We then introduce a populated-cell list (PC-list), a sorted list of non-empty cells of a grid (grid is used to index our data). Using PC-list, our top-k outlier de...

Outlier Detection is a major issue in data mining. Outliers are the containments that divert from the other objects. Outlier detection is used to make the data knowledgeable, and easy to understand. There are many type of databases used now days, and many of them contains anomaly objects, detection or removal of these objects is known as outlier detection. In the proposed work outliers are dete...

The increased nature of email spam with the use of urge mailing tools prompt the need for detector generation to counter the menace of unsolocited email. Detector generation inspired by the human immune system implements particle swarm optimization (PSO) to generate detector in negative selection algorithm (NSA). Outlier detectors are unique features generated by local outlier factor (LOF). The...