In parametric IC testing, outlier detection is applied to filter out potential unreliable devices. Most outlier detection methods are used in an offline setting and hence are not applicable to Final Test, where immediate pass/fail decisions are required. Therefore, we developed a new bivariate online outlier detection method that is applicable to Final Test without making assumptions about a sp...

PMML is an industry-standard XML-based open format for representing statistical and data mining models. Since PMML does not yet support outlier (anomaly) detection, in this paper we propose a new outlier detection model to foster interoperability in this emerging field. Our proposal is included in the PMML RoadMap for PMML 4.4. We demonstrate the proposed format on one supervised and two unsupe...

A procedure for detecting outliers in regression problems is proposed. It is based on information provided by boosting regression trees. The key idea is to select the most frequently resampled observation along the boosting iterations and reiterate after removing it. The selection criterion is based on Tchebychev’s inequality applied to the maximum over the boosting iterations of ...

Uncertain data management, querying and mining have become important because the majority of real world data is accompanied with uncertainty these days. Uncertainty in data is often caused by the deficiency in underlying data collecting equipments or sometimes manually introduced to preserve data privacy. The uncertainty information in the data is useful and can be used to improve the quality o...

This paper overviews and discusses our recent work on a multivariate conditional outlier detection framework for clinical applications.

Outlier detection aims to identify unusual data instances that deviate from expected patterns. The outlier detection is particularly challenging when outliers are context dependent and when they are defined by unusual combinations of multiple outcome variable values. In this paper, we develop and study a new conditional outlier detection approach for multivariate outcome spaces that works by (1...

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 is usually considered as a pre-processing step for locating in a data set, those objects that do not conform to well-defi ned notions of expected behaviour. It is very important in data mining for discovering novel or rare events, anomalies, vicious actions, exceptional phenomena etc. However, investigation of outlier detection for categorical data sets is especially a challen...