VOA*: Fast Angle-Based Outlier Detection over High-Dimensional Data Streams

Khalique, Vijdan; Kitagawa, Hiroyuki

doi:10.1007/978-3-030-75762-5_4

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…There are many outlier detection methods based on different observations for streaming data [2,8,16,18,22,39,42]. ILOF is the extension of LOF for data streams which can incrementally update the LOF scores of the points [33].…”

Section: Related Workmentioning

confidence: 99%

BPF: a novel cluster boundary points detection method for static and streaming data

2023

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Data points situated near a cluster boundary are called boundary points and they can represent useful information about the process generating this data. The existing methods of boundary points detection cannot differentiate boundary points from outliers as they are affected by the presence of outliers as well as by the size and density of clusters in the dataset. Also, they require tuning of one or more parameters and prior knowledge of the number of outliers in the dataset for tuning. In this research, a boundary points detection method called BPF is proposed which can effectively differentiate boundary points from outliers and core points. BPF combines the well-known outlier detection method Local Outlier Factor (LOF) with Gravity value to calculate the BPF score. Our proposed algorithm StaticBPF can detect the top-m boundary points in the given dataset. Importantly, StaticBPF requires tuning of only one parameter i.e. the number of nearest neighbors $$(k)$$ ( k ) and can employ the same $$k$$ k used by LOF for outlier detection. This paper also extends BPF for streaming data and proposes StreamBPF. StreamBPF employs a grid structure for improving k-nearest neighbor computation and an incremental method of calculating BPF scores of a subset of data points in a sliding window over data streams. In evaluation, the accuracy of StaticBPF and the runtime efficiency of StreamBPF are evaluated on synthetic and real data where they generally performed better than their competitors.

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Section: Related Workmentioning

confidence: 99%

BPF: a novel cluster boundary points detection method for static and streaming data

2023

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“…AD is the research area that studies the detection of anomalous observations through methods, models, and algorithms based on data [5]. AD about time series has been studied for decades, and numerous approaches have been addressed in previous studies [6], including angle-based outlier detection (ABOD) [7,8], local outlier factor (LOF) [9], support vector machines (SVMs) [10,11], Isolation Forest [12], and so forth.…”

Section: Introductionmentioning

confidence: 99%

Online detection of analog signal anomaly based on the evaluation of local trends

Liu,

Jiang,

2023

Meas. Sci. Technol.

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Online anomaly detection of analog signals plays an important role in equipment fault diagnosis and predictive maintenance. However, the signal often deviates slightly from those seen previously in the early stage of equipment failure, and the anomaly is invisible to the human eye. This kind of anomaly belongs to the typical contextual anomaly. Whether this anomaly can be effectively detected determines whether the failure of the equipment can be detected in the early stage, which is of great significance for safety in production. This study aimed to propose an online anomaly detection method for the analog signals of the quasi-sine waveform class. The sample similarity in the sliding window was evaluated using a sample trend rather than sample amplitude deviation to detect anomalies based on the principle that the trend of the quasi-sinusoid waveform signal in the adjacent space was similar. Compared with the traditional method, the proposed method was sensitive to contextual anomalies and did not need a complete sample data set for model training. The proposed method was finally validated by three data sets with good results.

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A Metaheuristic-Based Subspace Search Approach for Outlier Detection in High-Dimensional Data Streams

Souiden,

Brahmi,

Omri

2024

Advances in Science, Technology &Amp; Innovation

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VOA*: Fast Angle-Based Outlier Detection over High-Dimensional Data Streams

Cited by 4 publications

References 19 publications

BPF: a novel cluster boundary points detection method for static and streaming data

BPF: a novel cluster boundary points detection method for static and streaming data

Online detection of analog signal anomaly based on the evaluation of local trends

A Metaheuristic-Based Subspace Search Approach for Outlier Detection in High-Dimensional Data Streams

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