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...

Outliers detection is a task that finds objects that are dissimilar or inconsistent with respect to the remaining data. It has many uses in applications like fraud detection, network intrusion detection and clinical diagnosis of diseases. Using clustering algorithms for outlier detection is a technique that is frequently used. The clustering algorithms consider outlier detection only to the poi...

Efficient outlier detection in a large-sized big data environment incurs much of complexity in processing the information and to handle it in a proficient way. For segregating outliers from those normal data items, many of the prevailing methodologies experiences complexity in accordance with the features involved in every single attribute. On recognizing appropriate features associated the cha...

Benchmarking unsupervised outlier detection is difficult. Outliers are rare, and existing benchmark data contains outliers with various unknown characteristics. Fully synthetic usually consists of regular instances clear characteristics thus allows for a more meaningful evaluation methods in principle. Nonetheless, there have only been few attempts to include benchmarks detection. This might be...

Based on the idea of Weighted Spatial Outlier (WSO), this study identifies the influences of spatial attributes on the calculation of spatial outlying degree, combines these influences with non-spatial attributes, and proposes two revised spatial outlier detection algorithms, Improved Z-value (IZ-value) algorithm and Weighted Difference Algorithm (WDA). The proposed algorithms are detailed in t...

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...