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 ...

Outliers are very common in the environmental data monitored by a sensor network consisting of many inexpensive, low fidelity, and frequently failed sensors. The limited battery power and costly data transmission have introduced a new challenge for outlier cleaning in sensor networks: it must be done innetwork to avoid spending energy on transmitting outliers. In this paper, we propose an in-ne...

A novel approach to outlier detection on the ground of the properties of distribution of distances between multidimensional points is presented. The basic idea is to evaluate the outlier factor for each data point. The factor is used to rank the dataset objects regarding their degree of being an outlier. Selecting the points with the minimal factor values can then identify outliers. The main ad...

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...

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...

Outlier genes with marked overexpression in subsets of cancers like ERBB2 have potential for the identification of gene classifiers and therapeutic targets for the appropriate subpopulation. In this study, using the cancer outlier profile analysis strategy, we identified WNT1-inducible-signaling pathway protein 3 (WISP3) as an outlier gene that is highly expressed in a subset of colorectal canc...

In order to solve the defect in the spatial outlier mining algorithm that the spatial objects may be affected by their surrounding abnormal neighbors, a Based K-Nearest Neighbor (BKNN) algorithm was proposed based on the working principle of KNN Graph, which could effectively identify the spatial outliers by using cutting edge strategies. The core idea of BKNN is to calculate the dissimilarity ...

The Kalman filter is widely used in many different fields. Many practical applications and theoretical results show that the Kalman filter is very sensitive to outliers in a measurement process. In this paper some reasons why the Kalman Filter is sensitive to outliers are analyzed and a series of outlier-tolerant algorithms are designed to be used as substitutes of the Kalman Filter. These outl...

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 ...

Outlier detection is a very important type of data mining, which is extensively used in application areas. The traditional cell-based outlier detection algorithm not only takes a large amount of time in processing massive data, but also uses lots of machine resources, which results in the imbalance of the machine load. This paper presents an distancebased outlier detection algorithm. These expe...