• Anomaly detection in mixed high|dimensional molecular data🔍
• Robust Outlier Detection Technique in Data Mining🔍
• Multivariate Outlier Detection🔍
• Outlier Detection Through Connectivity|Based Outlier Factor for ...🔍
• A Novel Method for Cyber Threat Detection Based on Sliding ...🔍
• Time series outlier detection🔍
• SEGMENTING TIME SERIES🔍
• Charu C. Aggarwal Second Edition🔍

Anomaly detection in mixed high-dimensional molecular data - PMC

The literature knows numerous methods for detecting univariate outliers ... Therefore, we developed a novel approach to anomaly detection based on ...

Robust Outlier Detection Technique in Data Mining - NASA ADS

This paper describes an approach which uses Univariate outlier detection as a pre-processing step to detect the outlier and then applies K-means algorithm hence ...

Multivariate Outlier Detection: A Game Changer in Understanding ...

Univariate outlier detection, though useful, has its limitations. It analyzes data one variable at a time, missing the bigger picture. For ...

Outlier Detection Through Connectivity-Based Outlier Factor for ...

In (Afric et al., 2020) a novel statistical approach is proposed to predict ... ing around 63% for the univariate model and 69% for the multivariate ...

A Novel Method for Cyber Threat Detection Based on Sliding ...

On synthetic datasets, we implemented the accuracy technique to local outlier factor-based ways to evaluate it, and discovered that the ...

Time series outlier detection, a data-driven approach

By applying unsupervised machine learning techniques, we explore novel ways ... rely on a univariate method. Time series modelling. We fitted ARIMA models on ...

SEGMENTING TIME SERIES: A SURVEY AND NOVEL APPROACH

This representation has been used by various researchers to support clustering, classification, indexing and association rule mining of time series data. A ...

Charu C. Aggarwal Second Edition - Outlier Analysis

The classical books relevant to outlier analysis are as follows: • P. Rousseeuw and A. Leroy. Robust Regression and Outlier Detection, Wiley, 2003. • V ...

Detection of outliers method in grouped multivariate data based on ...

In this study, we propose a novel approach that combines the use of boxplots and Euclidean distance metrics for outlier identification.

Margin-based approach for outlier detection of industrial design data ...

The method creates a variable insensitive band surrounding the data cloud, interlinked with the normal regression function, providing upper and ...

DeepAnT: A Deep Learning Approach for Unsupervised Anomaly ...

They have provided two anomaly detection functions for detecting anomalies in seasonal univariate time series: (i) AnomalyDetectionTS function is used when ...

odetector: Outlier Detection Using Partitioning Clustering Algorithms

This package introduces the functions for some novel approaches to detect the outliers based on the typicality degrees, obtained using the fuzzy ...

A Novel Anomaly Detection Scheme Based on Principal Component ...

detected from the univariate criterion. Thus, all features need to be considered together using a multivariate approach. Let X1, X2, …, Xn be a random ...

Minimum-variance-based outlier detection method using ... - GMD

An influential outlier is a situation that, either independently or when combined with other biased observations, adversely affects the outcomes ...

Multiple Desirable Methods in Outlier Detection of Univariate Data ...

Outlier Detection Methods · Square Root Transformation · Median Absolute Deviation · Grubbs' Test · Ueda's Method.

Automatic Selection of Outlier Detection Techniques

This makes it much faster than other multivariate approaches, but we get less precision. Algorithm details. For every single feature (dimension), a univariate ...

Univariate Outlier Detection Using SAS

In such a case, detection of outliers may help evaluate if the statistical method is valid for its intended use. Finally, in some data analysis tasks, a dataset ...

Outlier Detection Technique for Univariate Normal Datasets

The study aimed at providing an alternative method that can be used in outlier detection for univariate normal data sets by deploying the measures of variation ...

2.7. Novelty and Outlier Detection — scikit-learn 1.5.2 documentation

Inliers are labeled 1, while outliers are labeled -1. The predict method makes use of a threshold on the raw scoring function computed by the estimator. This ...

What is the best way to identify outliers in multivariate data?

In multivariate analysis it is an observation removed from the bulk of the data. But what metric should we use to define extreme for the outlier ...