For nonrigid image registration, matching the particular structures (or the outliers) that have missing correspondence and/or local large deformations, can be more difficult than matching the common structures with small deformations in the two images. Most existing works depend heavily on the outlier segmentation to remove the outlier effect in the registration. Moreover, these works do not ha...

The role and importance of the industrial sector in the economic development specify the necessity of having accurate and timely data for exact planning. As outliers data in establishment surveys are common due to the structure of the economy, the evaluation of survey data by identifying and investigating outliers prior to the release of data is necessary. In this paper the practical applicatio...

Many different methods for statistical data editing can be found in the literature but only few of them are based on robust estimates (for example such as BACON-EEM, Epidemic algorithms (EA) and Transformed rank correlation (TRC) methods of Béguin and Hulliger). However, we can show that outlier detection is only reasonable if robust methods are applied, because the classical estimates are them...

Outlier identification often implies inspecting each z-transformed variable and adding a Mahalanobis D2. Multiple outliers may mask each other by increasing variance estimates. Caroni & Prescott (1992) proposed a multivariate extension of Rosner’s (1983) technique to circumvent masking, taking sample size into account to keep the false alarm risk below, say, α = .05. Simulations studies here co...

Outlier detection is an important task in data mining that enjoys a wide range of applications such as detections of credit card fraud, criminal activity and exceptional patterns in databases. In recent years, there have been numerous research work in outlier detection and the new notions such as distance-based outliers and density-based local outliers have been proposed. However, the existing ...

Multivariate sign functions are often used for robust estimation and inference. We propose using data dependent weights in association with such functions. The proposed weighted retain desirable robustness properties, while significantly improving efficiency inference compared to unweighted multivariate sign-based methods. Using signs, we demonstrate methods of location principal component anal...

We define the partial weighted set cover problem, a generic combinatorial optimization problem, that includes some classical data mining problems as special cases. We prove that it is computationally intractable and give a local search algorithm for this problem. As application examples, we then show how to translate clustering and outlier detection problems into this generic problem. Our exper...