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...

Clustering and outlier detection are important data mining areas. Online clustering and outlier detection generally work with continuous data streams generated at a rapid rate and have many practical applications, such as network instruction detection and online fraud detection. This chapter first reviews related background of online clustering and outlier detection. Then, an incremental cluste...

Outlier detection has significant importance in the data mining domain. Applications which contain streaming data flow may have many abnormal or outlier data and these applications require efficient outlier detection techniques to detect and analyze these abnormal patterns. Outlier detection is the process of detecting patterns in the data which do not adhere to the normal behavior or data. The...

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...

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 aims at searching for a small set of objects that are inconsistent or considerably deviating from other objects in a dataset. Existing research focuses on outlier identification while omitting the equally important problem of outlier interpretation. This paper presents a novel method named LODI to address both problems at the same time. In LODI, we develop an approach that exp...

We present definitions and properties of the fast massive unsupervised outlier detection (FastMUOD) indices, used for (OD) in functional data. FastMUOD detects outliers by computing, each curve, an amplitude, magnitude, shape index meant to target corresponding types outliers. Some methods adapting multivariate data are then proposed. These include applying on components using random projection...