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 ...

Outlier detection is an important process for text document collections, but as the collection grows, the detection process becomes a computationally expensive task. Random projection has shown to provide a good fast approximation of sparse data, such as document vectors, for outlier detection. The random samples of Fourier and cosine spectrum have shown to provide good approximations of sparse...

Outlier detection is a fundamental step in knowledge discovery in databases. With the increasing number of high-dimensional databases, existing outlier detection algorithms that work only in the context of full space are unable to effectively screen out informative outliers. This is because majority of these outliers exists only in subspaces. In this paper, we identify a new outlier detection t...

Protein kinases represent the most effective class of therapeutic targets in cancer; therefore, determination of kinase aberrations is a major focus of cancer genomic studies. Here, we analyzed transcriptome sequencing data from a compendium of 482 cancer and benign samples from 25 different tissue types, and defined distinct "outlier kinases" in individual breast and pancreatic cancer samples,...

The purpose of this article is to evaluate the application of forecasting models along with the use of residual control charts to assess production processes whose samples have autocorrelation characteristics. The main objective is to determine the efficiency of control charts for individual observations (CCIO) and exponentially weighted moving average (EWMA) charts when they are applied to res...