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...

The tendency for experimental and industrial variables to include a certain proportion of outliers has become a rule rather than an exception. These clusters of outliers, if left undetected, have the capability to distort the mean and the covariance matrix of the Hotelling's T2 multivariate control charts constructed to monitor individual quality characteristics. The effect of this distortion i...

There are several applications of combined phenological time series; e.g., trend analysis with long continuous time series, obtaining a compound and representative time series around weather stations for model fitting, data gap filling and outlier detection. Various methods to combine phenological time series have been proposed. We show that all of these methods can be analyzed within the theor...