Special Issue on Unsupervised Anomaly Detection

Goldstein, Markus

doi:10.3390/app13105916

Cited by 7 publications

(4 citation statements)

References 12 publications

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“…Goldstein's study provides a comprehensive overview of the current state and future directions of unsupervised anomaly detection [2]. This work is crucial in understanding the broad spectrum of applications and methodologies within this field.…”

Section: Related Workmentioning

confidence: 99%

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Unsupervised Anomaly Detection

Alnutefy,

Alsuwayh

2024

AI, Machine Learning and Applications

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This research focuses on Unsupervised Anomaly Detection using the "ambient_temperature_system_failure.csv" dataset from Numenta Anomaly Benchmark (NAB). The dataset contains time-series temperature readings from an industrial machine's sensor. The aim is to detect anomalies indicating system failures or aberrant behavior without labeled data. Various algorithms, such as K-means, Gaussian/Elliptic Envelopes, Markov Chain, Isolation Forest, One-Class SVM, and RNNs, are applied to analyze the temperature data. These algorithms are chosen for their ability to identify significant deviations in unlabeled datasets. The study explores how these techniques enhance anomaly understanding in time series data, relevant in manufacturing, healthcare, and finance. This research's novelty lies in employing unsupervised learning techniques on a real-world dataset and understanding theiradaptability in anomaly detection. The results are expected to contribute valuable insights to the field, showcasing the practicality and effectiveness of these algorithms across various scenarios.

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Section: Related Workmentioning

confidence: 99%

“…Goldstein's editorial contribution provides a panoramic view of the unsupervised anomaly detection landscape [2]. This work is instrumental in identifying the gaps and future research directions in the field.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Anomaly Detection

Alnutefy,

Alsuwayh

2024

AI, Machine Learning and Applications

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“…If so, then the time complexity of process (1) is n 2 • p • T (p = µ/n, 0 < p < 1, 1 < T n), where µ represents the number of samples in each sub-sample set, p represents the ratio of the number of samples in the sub-sample set to the total number of samples, T represents the number of sub-sample sets, and n represents the total number of samples. For process (2), the HPLOF algorithm has the same time complexity as LOF O n 2 . Therefore, its time complexity is also n 2 • p • T (p = µ/n, 0 < p < 1, 1 < T n).…”

Section: Experimental Contentsmentioning

confidence: 99%

“…Researchers from various disciplines, including statistics, big data, and machine learning, have shown a keen interest in outlier detection. Additionally, outlier detection plays a significant role in various applied domains, such as network intrusion detection in computer systems [ 2 , 3 , 4 ], fraud detection in credit card transactions [ 5 , 6 ], and anomaly detection in health insurance [ 7 , 8 ], to name just a few.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Outlier Detection Method Based on Information Entropy-Weighted Subspaces for High-Dimensional Data

Zhang

2023

Entropy

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Outlier detection is an important task in the field of data mining and a highly active area of research in machine learning. In industrial automation, datasets are often high-dimensional, meaning an effort to study all dimensions directly leads to data sparsity, thus causing outliers to be masked by noise effects in high-dimensional spaces. The “curse of dimensionality” phenomenon renders many conventional outlier detection methods ineffective. This paper proposes a new outlier detection algorithm called EOEH (Ensemble Outlier Detection Method Based on Information Entropy-Weighted Subspaces for High-Dimensional Data). First, random secondary subsampling is performed on the data, and detectors are run on various small-scale sub-samples to provide diverse detection results. Results are then aggregated to reduce the global variance and enhance the robustness of the algorithm. Subsequently, information entropy is utilized to construct a dimension-space weighting method that can discern the influential factors within different dimensional spaces. This method generates weighted subspaces and dimensions for data objects, reducing the impact of noise created by high-dimensional data and improving high-dimensional data detection performance. Finally, this study offers a design for a new high-precision local outlier factor (HPLOF) detector that amplifies the differentiation between normal and outlier data, thereby improving the detection performance of the algorithm. The feasibility of this algorithm is validated through experiments that used both simulated and UCI datasets. In comparison to popular outlier detection algorithms, our algorithm demonstrates a superior detection performance and runtime efficiency. Compared with the current popular, common algorithms, the EOEH algorithm improves the detection performance by 6% on average. In terms of running time for high-dimensional data, EOEH is 20% faster than the current popular algorithms.

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A Robust Outlier Detection Method in High-Dimensional Data Based on Mutual Information and Principal Component Analysis

Wang,

2024

Lecture Notes in Computer Science

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Special Issue on Unsupervised Anomaly Detection

Abstract: Anomaly detection (also known as outlier detection) is the task of finding instances in a dataset which deviate markedly from the norm [...]

Cited by 7 publications

References 12 publications

Unsupervised Anomaly Detection

Unsupervised Anomaly Detection

An Ensemble Outlier Detection Method Based on Information Entropy-Weighted Subspaces for High-Dimensional Data

A Robust Outlier Detection Method in High-Dimensional Data Based on Mutual Information and Principal Component Analysis

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