OBJECTIVE The Surgical Care Improvement Project (SCIP) has benchmarked 6:00 am blood glucose levels of less than 200 mg/dL on postoperative day (POD) 1 and 2 as quality measures of glycemic control in cardiac surgery. This study was undertaken to (1) determine the incidence of SCIP outliers in patients receiving a continuous insulin infusion (CII) targeted to maintain perioperative serum glucos...

Outliers are the points which are different from or inconsistent with the rest of the data. They can be novel, new, abnormal, unusual or noisy information. Outliers are sometimes more interesting than the majority of the data. The main challenges of outlier detection with the increasing complexity, size and variety of datasets, are how to catch similar outliers as a group, and how to evaluate t...

Outliers are the data objects that do not conform to the normal behaviour and usually deviates from the remaining data objects may be due to some outlying property which distinguishes them from the whole dataset. Usually, the detection of outliers is followed by the clustering of the dataset which sometimes ignores the prominency of outliers. In this study, we have tried to detect the outliers ...

Graduate School of Systems and Information Engineering, University of Tsukuba Institute of Information Sciences and Electronics, University of Tsukuba School of Computer Science, Carnegie Mellon University Abstract Outlier detection has many applications like fraud detection, medical analysis, etc. Recently, several methods for finding outliers in large datasets have been reported. These existi...

This article aims to provide a simple robust method to test the parameters of a normal population by using the new diagnostic tool called the “Forward Search” (FS) method. The most commonly used procedures to test the mean and variance of a normal distribution are Student’s t test and Chi-square test, respectively. These tests suffer from the presence of outliers. We introduce the FS version of...

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 ...

In this paper, a new approach is presented to fit arobust fuzzy regression model based on some fuzzy quantities. Inthis approach, we first introduce a new distance between two fuzzynumbers using the kernel function, and then, based on the leastsquares method, the parameters of fuzzy regression model isestimated. The proposed approach has a suitable performance to<b...

Mining outliers in database is to find exceptional objects that deviate from the rest of the data set. Besides classical outlier analysis algorithms, recent studies have focused on mining local outliers, i.e., the outliers that have density distribution significantly different from their neighborhood. The estimation of density distribution at the location of an object has so far been based on t...

Identiication of outliers can lead to the discovery of unexpected, interesting, and implicit knowledge. Existing methods are designed for detecting spatial outliers in multidimensional geometric data sets, where a distance metric is available. In this paper, we focus on detecting spatial outliers in graph structured data sets. We deene tests for spatial outliers in graph structured data sets, a...

Spatial outliers represent locations which are signiicantly diierent from their neighborhoods even though they may not be signiicantly diierent from the entire population. Identiication of spatial outliers can lead to the discovery of unexpected , interesting, and implicit knowledge, such as local instability. In this paper, we rst provide a general deenition of S-outliers for spatial outliers....