A Novel Approach for Univariate Outlier Detection

A Novel Approach for Univariate Outlier Detection - CiteSeerX

Experimental results show that our method works well for different data. Index Terms— Grubbs Test, Masking , Sigma rule, Univariate Outlier Detection. 1 ...

[PDF] A Novel Approach for Univariate Outlier Detection | Semantic ...

Grubb's statistics, sigma rule and fence rules deal more than one outliers at a time for detecting outliers in univariate data sets when multiple outliers ...

A novel outlier detection approach for univariate datasets using ...

A novel outlier detection approach for univariate datasets using deep neural networks. Olgun Aydin1. Gdansk University of Technology, Poland. Abstract. An ...

Univariate Outlier Detection: Precision-Driven Algorithm for Single ...

The approach leverages a combination of transformative techniques and advanced filtration methods to efficiently segregate anomalies from normal values. Notably ...

A Novel Approach for Outlier Detection in Multivariate Data - Afzal

In this paper, we develop and evaluate a new method for the detection of outliers in multivariate data that relies on Principal Components Analysis (PCA) and ...

Detecting outliers in a univariate time series dataset using ...

The coastal ocean temperature dataset is univariate, and researchers found that statistical approaches are the most efficient unsupervised algorithms at ...

A novel approach for outlier detection and clustering improvement

This paper provides a novel unsupervised approach to detect outliers using a modified k-means clustering algorithm and shows that the proposed technique ...

A novel outlier detection approach based on formal concept analysis

Experiments are performed on fifteen public datasets, and then our algorithm is compared with classical and rough set-based outlier detection ...

A New Approach for Outlier Detection in Near Real Time - IEEE Xplore

Parametric (statistical) and non parametric methods combined with univariate and multivariate methods form most of the body of research in anomaly detection. In ...

A novel outlier detection approach based on formal concept analysis

Experiments are performed on fifteen public datasets, and then our algorithm is compared with classical and rough set-based outlier detection algorithms. The ...

A novel approach for outlier detection and clustering improvement

In this paper, we provide a novel unsupervised approach to detect outliers using a modified k-means clustering algorithm. The detected outliers are removed from ...

Two novel outlier detection approaches based on unsupervised ...

The proposed approaches are based on finding the atypical data points below a predefined threshold value, a possibilistic level for evaluating a ...

Outlier detection for mixed-type data: A novel approach

In this paper we propose a novel method that detects outlying observations in settings of mixed-type data, while reducing the required user ...

Data-driven cluster analysis method: a novel outliers detection ...

This study proposes a new technique for detecting multivariate outliers based on cluster analysis. The method considers information inherent in the data itself.

A Novel Approach for Outlier Detection in Multivariate Data

Abstract. Outlier detection is a challenging task especially when outliers are defined by rare combinations of multiple variables. In this paper, we develop and ...

[2308.09562] Outlier detection for mixed-type data: A novel approach

In this paper we propose a novel method that detects outlying observations in settings of mixed-type data, while reducing the required user interaction.

A novel approach for detecting outliers by using Isolation Forest with ...

In this research, we attempted to use the isolation forest approach to calculate the outlier factor. Then a model known as an outlier finding model is created.

An Effective Approach to Outlier Detection Based on Centrality and ...

Univariate outliers are connected to a distribution of values in a single feature space while multivariate outliers are connected to an n-dimensional space (of ...

A Novel Outlier Detection Method for Multivariate Data - IEEE Xplore

A Novel Outlier Detection Method for Multivariate Data. Abstract: Detecting anomalous objects from given data has a broad range of real-world ...

An Effective Approach to Outlier Detection Based on Centrality and ...

Univariate outliers are connected to a distribution of values in a single feature space while multivariate outliers are connected to an n- ...