Now day’s Outlier Detection is used in various fields such as Credit Card Fraud Detection, Cyber-Intrusion Detection, Medical Anomaly Detection, and Data Mining etc. So to detect anomaly objects from various types of dataset Outlier Detection techniques are used, that detects and remove the anomaly objects from the dataset. Outliers are the containments that divert from the other objects. Outli...

Data association is an important data-mining task and it has various applications. In crime analysis, data association means to link criminal incidents committed by the same person. It helps to discover crime patterns and catch the criminal. In this paper, we present an outlier-based data association method. An outlier score function is defined to measure the extremeness of an observation, and ...

In this paper the task of identifying outliers in exponential samples is treated conceptionally in the sense of Davies and Gather by means of a so called outlier region In case of an exponential distribution an empirical approximation of such a region also called an outlier identi er is mainly dependent on some estimator of the unknown scale parameter The worst case behaviour of several reasona...

Outlier detection amounts to finding data points that differ significantly from the norm. Classic outlier detection methods are largely designed for single data type such as continuous or discrete. However, real world data is increasingly heterogeneous, where a data point can have both discrete and continuous attributes. Handling mixed-type data in a disciplined way remains a great challenge. I...

We propose a detection method of corner outlier artifacts and a simple and effective filter in order to remove the artifacts in highly compressed video. We detect the corner outlier artifacts based on the direction of edges going through a block corner and the properties of blocks around the edges. Based on the detection results, we remove the stair-shaped discontinuities, i.e., corner outlier ...

Frequent pattern outlier factor is used to detect outliers with complete frequent itemsets. But it is difficult in real-world time-series data streams application because of its low efficiency. In this paper, we propose a novel maximal frequent pattern outlier factor (MFPOF) and an outlier detection algorithm (OODFP) for online high-dimensional time-series outlier detection. Firstly, the time-s...

This paper deals with outlier modeling within a very special framework: a segment-based speech recognizer. The recognizer is built on a neural net that, besides classifying speech segments, has to identify outliers as well. One possibility is to artificially generate outlier samples, but this is tedious, error-prone and significantly increases the training time. This study examines the alternat...

Outlier Mining has always attract much attention among the data mining community. This paper discusses on the discovery of meaningful outlier detection based on Non_Reduct computation by incorporating the Negative Association Rules. Non_Reduct computation is proposed to detect outliers from rare classes. These outliers may have meaningful knowledge having incorporating the concept of Negatives ...

Outlier or anomaly detection in large data sets is a fundamental task in data science, with broad applications. However, in real data sets with high-dimensional space, most outliers are hidden in certain dimensional combinations and are relative to a user's search space and interest. It is often more effective to give power to users and allow them to specify outlier queries flexibly, and the sy...

We searched a gene expression dataset comprised of 634 schizophrenia (SZ) cases and 713 controls for expression outliers (i.e., extreme tails of the distribution of transcript expression values) with SZ cases overrepresented compared with controls. These outlier genes were enriched for brain expression and for genes known to be associated with neurodevelopmental disorders. SZ cases showed highe...