Search Results - (( java application using algorithm ) OR ( outlier detection using algorithm ))
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The multiple outliers detection for circular univariate data using different agglomerative clustering algorithms
Published 2024“…Meanwhile, SL-Satari/Di, CL-Satari/Di, and AL-Satari/Di algorithms are recommended to be used for large sample sizes since these algorithms perform very well in detecting the outliers and have low masking and swamping effect at any percentage of outliers and concentration parameter. …”
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2
Outlier detection in circular regression model using minimum spanning tree method
Published 2019“…Therefore, this study aims to develop new algorithms that can detect outliers by using the minimum spanning tree method. …”
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3
The effect of different distance measures in detecting outliers using clustering-based algorithm for circular regression model
Published 2017“…In this study, we proposed multiple outliers detection in circular regression models based on the clustering algorithm. …”
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4
Comparative study of clustering-based outliers detection methods in circular-circular regression model
Published 2021“…This paper is a comparative study of several algorithms for detecting multiple outliers in circular-circular regression model based on the clustering algorithms. …”
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5
Single-linkage method to detect multiple outliers with different outlier scenarios in circular regression model
Published 2018“…Single-linkage is one of the algorithms in agglomerative clustering technique that can be used to detect outliers. …”
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6
Comparative study of clustering-based outliers detection methods in circular-circular regression model
Published 2021“…This paper is a comparative study of several algorithms for detecting multiple outliers in circular-circular regression model based on the clustering algorithms. …”
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7
Comparative study of clustering-based outliers detection methods in circularcircular regression model
Published 2021“…This paper is a comparative study of several algorithms for detecting multiple outliers in circular-circular regression model based on the clustering algorithms. …”
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8
The effect of different similarity distance measures in detecting outliers using single-linkage clustering algorithm for univariate circular biological data
“…Clustering algorithms can be used to create an outlier detection procedure in univariate circular data. …”
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9
Adaptive grid-meshed-buffer clustering algorithm for outlier detection in evolving data stream
Published 2023“…This research introduces Adaptive Grid-Meshed-Buffer Stream Clustering Algorithm (AGMB), that addresses these weaknesses and improves outlier detection. …”
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10
Automatic filtering of far outliers in multibeam echo sounding dataset using robust detection algorithms
Published 2005“…This paper elaborates the techniques used for the detection and elimination of the far outliers in the MBES dataset, known as robust detection algorithms. …”
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11
Parameter estimation and outlier detection in linear functional relationship model / Adilah Abdul Ghapor
Published 2017“…The simulation results indicate that the proposed method is suitable to detect a single outlier. As for the multiple outliers, a clustering algorithm is considered and a dendogram to visualise the clustering algorithm is used. …”
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12
The Multiple Outliers Detection using Agglomerative Hierarchical Methods in Circular Regression Model
Published 2017“…The single-linkage method is one of the simplest agglomerative hierarchical methods that is commonly used to detect outlier. In this study, we compared the performance of single-linkage method with another agglomerative hierarchical method, namely average linkage for detecting outlier in circular regression model. …”
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13
Machine Learning Approaches to Advanced Outlier Detection in Psychological Datasets
Published 2025“…In conclusion, while individual algorithms provide distinct perspectives, ensemble techniques enhance the accuracy and consistency of outlier detection. …”
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Detection of multiple outliners in linear regression using nonparametric methods
Published 2004“…However, most of them are complicated and unappealing to users with no mathematical background. The clustering algorithm from Sebert et al. (1998) is discussed and used since it is easy to understand with interesting proposed approach and have a good performance in detecting the presence of outliers. …”
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15
Dynamic Robust Bootstrap Algorithm for Linear Model Selection Using Least Trimmed Squares
Published 2009“…To rectify this problem, we propose a Dynamic Robust Bootstrap-LTS based (DRBLTS) algorithm where the percentage of outliers in each bootstrap sample is detected. …”
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16
Dissimilarity algorithm on conceptual graphs to mine text outliers
Published 2009“…In Comparison to other text outlier detection method, this approach managed to capture the semantics of documents through the use of CGs and is convenient to detect outliers through a simple dissimilarity function.Furthermore, our proposed algorithm retains a linear complexity with the increasing number of CGs.…”
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17
Outlier detection in circular regression model using minimum spanning tree method
Published 2019“…Therefore, this study aims to develop new algorithms that can detect outliers by using minimum spanning tree method. …”
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18
Winsorize tree algorithm for handling outliers in classification problem
Published 2016“…The upper fence and lower fence of a boxplot are used to detect potential outliers whose values exceeding the tail of Q ± (1.5×Interquartile range). …”
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19
A systematic literature review on outlier detection in wireless sensor networks
Published 2020“…The current paper presents an improved taxonomy of outlier detection techniques. This will help researchers and practitioners to find the most relevant and recent studies related to outlier detection in WSNs. …”
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Improved robust estimator and clustering procedures for multivariate outliers detection
Published 2023“…One of the methods to detect outliers in multivariate data is by using distance-based methods, which is Mahalanobis distance (MD). …”
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