Outlier detection is an important research problem in data mining that aims to find objects that are considerably dissimilar, exceptional and inconsistent with respect to the majority data in an input database [50]. Outlier detection, also known as anomaly detection in some literatures, has become the enabling underlying technology for a wide range of practical applications in industry, busines...

We present a novel resolution-based outlier notion and a nonparametric outlier-mining algorithm, which can efficiently identify top listed outliers from a wide variety of datasets. The algorithm generates reasonable outlier results by taking both local and global features of a dataset into consideration. Experiments are conducted using both synthetic datasets and a real life construction equipm...

Outlier detection is a very important type of data mining, which is extensively used in application areas. The traditional cell-based outlier detection algorithm not only takes a large amount of time in processing massive data, but also uses lots of machine resources, which results in the imbalance of the machine load. This paper presents an distancebased outlier detection algorithm. These expe...

Summarization requires selection of the more informative sentences within a set of documents. Generally, process assumes the document set includes related topics to a subject. However, some of the documents may be outlier and the effect of an outlier document might affect the success of extractive summary. Research is focused on filtering documents at the extraction stage these are outlier. Ext...

In this paper a novel Support vector clustering(SVC) method for outlier detection is proposed. Outlier detection algorithms have application in several tasks such as data mining, data preprocessing, data filter-cleaner, time series analysis and so on. Traditionally outlier detection methods are mostly based on modeling data based on its statistical properties and these approaches are only prefe...

In this paper, we address the problem of detecting outlier samples with highly different expression patterns in microarray data. Although outliers are not common, they appear even in widely used benchmark data sets and can negatively affect microarray data analysis. It is important to identify outliers in order to explore underlying experimental or biological problems and remove erroneous data....

Outlier detection is an important problem that has applications in many fields. High dimensional datasets are common in such applications. Among the existing outlier detection methods, Distance-Based outlier (DB-Outlier) detection is one of the most generalizable and simplest approaches. It finds outliers by calculating distances between data points. However, in high dimensional space, data dis...

Recent research in motion detection has shown that various outlier detection methods could be used for efficient detection of small moving targets. These algorithms detect moving objects as outliers in a properly defined attribute space, where outlier is defined as an object distinct from the objects in its neighborhood. In this paper, we compare the performance of two incremental outlier detec...

We developed a quality assurance (QA) tool, namely microarray outlier filter (MOF), and have applied it to our microarray datasets for the identification of problematic arrays. Our approach is based on the comparison of the arrays using the correlation coefficient and the number of outlier spots generated on each array to reveal outlier arrays. For a human universal reference (HUR) dataset, whi...