Classical methods for detecting outliers deal with continuous variables. These methods are not readily applicable to categorical data, such as incorrect/correct scores (0/1) and ordered rating scale scores (e.g., 0, . . . , 4) typical of multi-item tests and questionnaires. This study proposes two definitions of outlier scores suited for categorical data. One definition combines information on ...

In high dimensional data large no of outliers are embedded in low dimensional subspaces known as projected outliers, but most of existing outlier detection techniques are unable to find these projected outliers, because these methods perform detection of abnormal patterns in full data space. So, outlier detection in high dimensional data becomes an important research problem. In this paper we a...

We analyse the interplay of density estimation and outlier detection in density-based outlier detection. By clear and principled decoupling of both steps, we formulate a generalization of density-based outlier detection methods based on kernel density estimation. Embedded in a broader framework for outlier detection, the resulting method can be easily adapted to detect novel types of outliers: ...

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...

It is well known that if a multivariate outlier has one or more missing component values, then multiple imputation methods tend to impute non-extreme values and make the outlier become less extreme and less likely to be detected. In this paper, nonparametric depthbased multivariate outlier identifiers are used as criteria in a numerical study comparing several established methods of multiple im...

Current researches focused on gene function classification and discovery are with the use of wet laboratory. This research focused on the identification of outlier yeast genes, Saccharomyces cerevisiae involved in a eukaryotic cell cycle using time series normalized gene expression data. A method for identifying outlier genes using Nonmetric Multidimensional Scaling (nMDS) with confidence inter...