In their paper, Davies and Gather (1993) formalized the task of outlier identiica-tion, considering also certain performance criteria for outlier identiiers. One of those criteria, the maximum asymptotic bias, is carried over here to multivariate outlier identiiers. We show how this term depends on the respective biases of estimators which are used to construct the identiier. It turns out that ...

Outliers can significantly affect data mining performance. Outlier mining is an important issue in knowledge discovery and data mining and has attracted increasing interests in recent years. Class outlier is promising research direction. Few researches have been done in this direction. The paper theme has two main goals: the first one is to show the significance of Class Outlier Mining by discu...

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...

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....

The purpose of this paper is to identify the effective points on the performance of one of the important algorithm of data mining namely support vector machine. The final classification decision has been made based on the small portion of data called support vectors. So, existence of the atypical observations in the aforementioned points, will result in deviation from the correct decision. Thus...

Outlier detection has been a very important concept in data mining. The aim of outlier detection is to find those objects that are of not the norm. There are many applications of outlier detection from network security to detecting credit fraud. However most of the outlier detection algorithms are focused towards numerical data and do not perform well when applied to categorical data. In this p...

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...

Outlier detection aims at searching for a small set of objects that are inconsistent or considerably deviating from other objects in a dataset. Existing research focuses on outlier identification while omitting the equally important problem of outlier interpretation. This paper presents a novel method named LODI to address both problems at the same time. In LODI, we develop an approach that exp...

In this paper, we introduce autoencoder ensembles for unsupervised outlier detection. One problem with neural networks is that they are sensitive to noise and often require large data sets to work robustly, while increasing data size makes them slow. As a result, there are only a few existing works in the literature on the use of neural networks in outlier detection. This paper shows that neura...