Figure 1. Demonstration of outlier detection based on a simulation data. (a) shows the noise-free peanut-shaped ADC spatial profile obtained from a 2D tensor. (b) depicts results with an extreme outlier. The dotted line represents the measurement data, while the solid line is the fitted ADC profile. (c) shows the error map between the measured ADC and the fitted ADC signals. It clearly demonstr...

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...

UNLABELLED The purpose of this research was to examine the distribution of the tooth size in a large sample. The objective teeth were the left upper and lower fourteen teeth except the third molar. The tooth size of 1,000 dental casts from the Japanese female orthodontic patients was measured. On each of them, a histogram and a set of statistics (mean, standard deviation, coefficient of variati...

From the past decade outlier detection has been in use. Detection of outliers is an emerging topic and is having robust applications in medical sciences and pharmaceutical sciences. Outlier detection is used to detect anomalous behaviour of data. Typical problems in Bioinformatics can be addressed by outlier detection. A computationally fast method for detecting outliers is shown, that is parti...

ABSTRACT-In Data mining there are lots of methods are used to detect the outlier by making the clusters of data and then detect the outlier from them. In general Clustering method plays a very important role in data mining. Clustering means grouping the similar data objects together based on the characteristic they possess. Outlier Detection is an important issue in Data mining; particularly it...

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...

This paper introduces a novel wrapper-based outlier detection framework (WrapperOD) and its instance (HOUR) for identifying outliers in noisy data (i.e., data with noisy features) with strong couplings between outlying behaviors. Existing subspace or feature selection-based methods are significantly challenged by such data, as their search of feature subset(s) is independent of outlier scoring ...

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...

This paper unifies “line-process” approaches for regularization with discontinuities and robust estimation techniques. We generalize the notion of a “line process” to that of an analog “outlier process” and show that a problem formulated in terms of outlier processes can be viewed an terms of robust statistics. We also characterize a class of robust statistical problems for which an equivalent ...