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...

Continuous outlier detection in data streams has important applications in fraud detection, network security, and public health. The arrival and departure of data objects in a streaming manner impose new challenges for outlier detection algorithms, especially in time and space efficiency. In the past decade, several studies have been performed to address the problem of distance-based outlier de...

Patterns that appear rarely or unusually in the data can be defined as outlier patterns. The basic idea behind detecting outlier patterns is comparison of their relative frequencies with frequent patterns. Their frequencies of appearance are less and thus have lesser support in the data. Detecting outlier patterns is an important data mining task which will reveal some interesting facts. The se...

Patterns that appear rarely or unusually in the data can be defined as outlier patterns. The basic idea behind detecting outlier patterns is comparison of their relative frequencies with frequent patterns. Their frequencies of appearance are less and thus have lesser support in the data. Detecting outlier patterns is an important data mining task which will reveal some interesting facts. The se...

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...

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 one of the major challenges in wireless sensor networks. To the best of our knowledge, not many have evaluated the performance of outlier detection algorithms in real time. This paper proposes a real time hardware implementation of outlier detection using locality sensitive hashing algorithm for temperature data and evaluated both in indoor and outdoor environments. NI WSN ...

We propose a new approach for outlier detection, based on a new ranking measure that focuses on the question of whether a point is “important” for its nearest neighbors; using our notations low cumulative rank implies the point is central. For instance, a point centrally located in a cluster has relatively low cumulative sum of ranks because it is among the nearest neighbors of its own nearest ...

To improve the accuracy and efficiency of space-time analysis, spatio-temporal neighbourhoods (STNs) should be investigated and analysed in the classification, prediction and outlier detection of space-time data. So far most researches in space-time analysis use either spatial or temporal neighbourhoods, without considering both time and space at the same time. Moreover, the neighbourhoods are ...

We propose a detection method of corner outlier artifacts and a simple and effective filter in order to remove the artifacts in highly compressed video. We detect the corner outlier artifacts based on the direction of edges going through a block corner and the properties of blocks around the edges. Based on the detection results, we remove the stair-shaped discontinuities, i.e., corner outlier ...