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...

Univariate outlier analysis has become a popular approach for improving quality. When a customer return occurs, multivariate outlier analysis extends the univariate analysis to develop a test model for preventing similar returns from happening. In this context, this work investigates the following question: How simple multivariate outlier modeling can be? The interest for answering this questio...

Outlier detection is one of the obstacles of big dataset analysis because of its time consumption issues. This paper proposes a fast outlier detection method for big datasets, which is a combination of cell-based algorithms and a ranking-based algorithm with various depths. A cell-based algorithm is proposed to transform a very large dataset to a fairly small set of weighted cells based on pred...