‘‘One person’s noise is another person’s signal” (Knorr, E., Ng, R. (1998). Algorithms for mining distancebased outliers in large datasets. In Proceedings of the 24th VLDB conference, New York (pp. 392–403)). In recent years, much attention has been given to the problem of outlier detection, whose aim is to detect outliers – objects which behave in an unexpected way or have abnormal properties....

Outlier is defined as an observation that deviates too much from other observations. The identification of outliers can lead to the discovery of useful and meaningful knowledge. Outlier detection has been extensively studied in the past decades. However, most existing research focuses on the algorithm based on special background, compared with outlier detection approach is still rare. Most soph...

Outlier Detection is a major issue in data mining. Outliers are the containments that divert from the other objects. Outlier detection is used to make the data knowledgeable, and easy to understand. There are many type of databases used now days, and many of them contains anomaly objects, detection or removal of these objects is known as outlier detection. In the proposed work outliers are dete...

Outlier detection has recently become an important problem in many data mining applications. In this paper, a novel unsupervised algorithm for outlier detection is proposed. First we apply a provably globally optimal Expectation Maximization (EM) algorithm to fit a Gaussian Mixture Model (GMM) to a given data set. In our approach, a Gaussian is centered at each data point, and hence, the estima...

The term “outlier" can generally be defined as an observation that is significantly different from the other values in a data set. The outliers may be instances of error or indicate events. The task of outlier detection aims at identifying such outliers in order to improve the analysis of data and further discover interesting and useful knowledge about unusual events within numerous application...

The term “outlier" can generally be defined as an observation that is significantly different from the other values in a data set. The outliers may be instances of error or indicate events. The task of outlier detection aims at identifying such outliers in order to improve the analysis of data and further discover interesting and useful knowledge about unusual events within numerous application...

Detecting outliers in mixed attribute datasets is one of major challenges in real world applications. Existing outlier detection methods lack effectiveness for mixed attribute datasets mainly due to their inability of considering interactions among different types of, e.g., numerical and categorical attributes. To address this issue in mixed attribute datasets, we propose a novel Pattern based ...

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...

Figure 1. Demonstration of outlier detection based on a simulation data. (a) shows the noise-free peanut-shaped ADC spatial profile obtained from a 2D tensor. (b) depicts results with an extreme outlier. The dotted line represents the measurement data, while the solid line is the fitted ADC profile. (c) shows the error map between the measured ADC and the fitted ADC signals. It clearly demonstr...