In this article, we present a simple multivariate outlier-detection procedure and a robust estimator for the covariance matrix, based on the use of information obtained from projections onto the directions that maximize and minimize the kurtosis coefŽ cient of the projected data. The properties of this estimator (computational cost, bias) are analyzed and compared with those of other robust est...

In this article we use projection pursuit methods to develop a procedure for detecting outliers in a multivariate time series. We show that testing for outliers in some projection directions can be more powerful than testing the multivariate series directly. The optimal directions for detecting outliers are found by numerical optimization of the kurtosis coefficient of the projected series. We ...

Robust distances are mainly used for the purpose of detecting multivariate outliers. The precise definition of cut-off values for formal outlier testing assumes that the “good” part of the data comes from a multivariate normal population. Robust distances also provide valuable information on the units not declared to be outliers and, under mild regularity conditions, they can be used to test th...

We propose an online and local outlier detection technique with low resource consumption based on an unsupervised centered quartersphere support vector machine for wireless sensor networks. Using synthetic data, we demonstrate that our technique achieves better mining performance in terms of parameter selection using difference kernel functions compared to an earlier offline outlier detection t...

Outlier detection is an important task in many data-mining applications. In this paper, we present two parametric outlier detection methods for survival data. Both methods propose to perform outlier detection in a multivariate setting, using the Cox regression as the model and the concordance c-index as a measure of goodness of fit. The first method is a single-step procedure that presents a de...

We consider the problem of finding outliers in large multivariate databases. Outlier detection can be applied during the data cleansing process of data mining to identify problems with the data itself, and to fraud detection where groups of outliers are often of particular interest. We use replicator neural networks (RNNs) to provide a measure of the outlyingness of data records. The performanc...

Outlier detection on time series data plays an import role in life. In this paper we propose a method of outlier detection on time series data mainly aiming at the multivariate type. The improved ant colony algorithm is used for data clustering in the purpose of the classification of the time series data. Both the distance of inner-clusters and inter-clusters are considered to ensure the accura...

In order to solve the defect in the spatial outlier mining algorithm that the spatial objects may be affected by their surrounding abnormal neighbors, a Based K-Nearest Neighbor (BKNN) algorithm was proposed based on the working principle of KNN Graph, which could effectively identify the spatial outliers by using cutting edge strategies. The core idea of BKNN is to calculate the dissimilarity ...

While researchers and practitioners may seamlessly develop methods of detecting outliers in control charts under a univariate setup, screening multivariate pose serious challenges. In this study, we propose robust chart based on the Stahel-Donoho estimator (SDRE), whilst process parameters are estimated from phase-I. Through intensive Monte-Carlo simulation, study presents how estimation presen...