An efficient outlier detection approach on weighted data stream based on minimal rare pattern mining

Cai, Saihua; Sun, Ruizhi; Hao, Shangbo; Li, Sicong; Yuan, Gang

doi:10.23919/jcc.2019.10.006

Cited by 18 publications

(11 citation statements)

References 18 publications

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“…May also struggle to identify outliers in datasets with nonuniformly distributed data An efficient outlier detection approach on weighted data stream based on minimal rare pattern mining-2019 Cai et al [16] presents a new method for outlier detection in weighted data streams Low computational complexity and high Scalability Incorrect choice of algorithm can be challenging in some cases., Difficulty in defining "rare patterns" Weighted Outlier Detection of High-Dimensional Categorical Data Using Feature Grouping-2020 Li et al [17] Presents a new method for outlier detection in highdimensional categorical data.…”

Section: Improved Accuracy Of Outlier Detection Compared To Tradition...mentioning

confidence: 99%

WePaMaDM-Outlier Detection: Weighted Outlier Detection using Pattern Approaches for Mass Data Mining

Verma

Jain²,

Bhavsar

2023

2023 International Conference on Advancement in Computation &Amp; Computer Technologies (InCACCT)

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Weighted Outlier Detection is a method for identifying unusual or anomalous data points in a dataset, which can be caused by various factors like human error, fraud, or equipment malfunctions. Detecting outliers can reveal vital information about system faults, fraudulent activities, and patterns in the data, assisting experts in addressing the root causes of these anomalies. However, creating a model of normal data patterns to identify outliers can be challenging due to the nature of input data, labeled data availability, and specific requirements of the problem. This article proposed the WePaMaDM-Outlier Detection with distinct mass data mining domain, demonstrating that such techniques are domain-dependent and usually developed for specific problem formulations. Nevertheless, similar domains can adapt solutions with modifications. This work also investigates the significance of data modeling in outlier detection techniques in surveillance, fault detection, and trend analysis, also referred to as novelty detection, a semisupervised task where the algorithm learns to recognize abnormality while being taught the normal class.

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Section: Improved Accuracy Of Outlier Detection Compared To Tradition...mentioning

confidence: 99%

WePaMaDM-Outlier Detection: Weighted Outlier Detection using Pattern Approaches for Mass Data Mining

Verma

Jain²,

Bhavsar

2023

2023 International Conference on Advancement in Computation &Amp; Computer Technologies (InCACCT)

View full text Add to dashboard Cite

show abstract

“…For the detection of data conflict in multisource data fusion, the abnormal points in conflict are regarded as outliers, and the point outlier detection technology is used to detect and process the conflict [19][20][21][22]. In the traditional data mining work, outlier detection is carried out by using statistics, clustering, classification, proximity, and other methods [23][24][25][26][27][28][29]. These methods are strong, simple, and direct but need to rely on a certain prior knowledge, and processing effects are directly affected by the level of knowledge.…”

Section: Related Workmentioning

confidence: 99%

A Credit Conflict Detection Model Based on Decision Distance and Probability Matrix

Zhang

Sun

2022

Wireless Communications and Mobile Computing

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Considering the credit index calculation differences, semantic differences, false data, and other problems between platforms such as Internet finance, e-commerce, and health and elderly care, which lead to the credit deviation from the trusted range of credit subjects and the lack of related information of credit subjects, in this paper, we proposed a crossplatform service credit conflict detection model based on the decision distance to support the migration and application of crossplatform credit information transmission and integration. Firstly, we give a scoring table of influencing factors. Score is the probability of the impact of this factor on credit. Through this probability, the distance matrix between influencing factors is generated. Secondly, the similarity matrix is calculated from the distance matrix. Thirdly, the support vector is calculated through the similarity matrix. Fourth, the credit vector is calculated by the support vector. Finally, the credibility is calculated by the credit vector and probability.

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“…Conventional outlier detection method however does not take into consideration the subset occurrence frequency and hence, the outliers being detected do not fit the definition of outliers. To address on this aspect, a two-phase minimal weighted rare pattern mining-based outlier detection method, called MWRPM-Outlier [4] was proposed to efficiently detect outliers based on the weight data stream.…”

Section: Related Workmentioning

confidence: 99%

Gaussian Projection Deep Extreme Clustering and Chebyshev Reflective Correlation based Outlier Detection

Rajalakshmi¹,

Madhubala²

2022

IJACSA

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Outlier detection or simply the task of point detection that are noticeably distinct and different from data sample is a predominant issue in deep learning. When a framework is constructed, these distinctive points can later lead to model training and compromise accurate predictions. Owing to this reason, it is paramount to recognize and eliminate them before constructing any supervised model and this is frequently the initial step when dealing with a deep learning issue. Over the recent few years, different numbers of outlier detector algorithms have been designed that ensure satisfactory results. However, their main disadvantages remain in the time and space complexity and unsupervised nature. In this work, a clusteringbased outlier detection called, Random Projection Deep Extreme Learning-based Chebyshev Reflective Correlation (RPDEL-CRC) is proposed. First, Gaussian Random Projection-based Deep Extreme Learning-based Clustering model is designed. Here, by applying Gaussian Random Projection function to the Deep Extreme Learning obtains the relevant and robust clusters corresponding to the data points in a significant manner. Next, with the robust clusters, outlier detection time is said to be reduced to a greater extent. In addition, a novel Chebyshev Temporal and Reflective Correlation-based Outlier Detection model is proposed to detect outliers therefore achieving high outlier detection accuracy. The proposed approach is validated with the NIFTY-50 stock market dataset. The performance of the RPDEL-CRC method is evaluated by applying it to NIFTY-50 Stock Market dataset. Finally, we compare the results of the RPDEL-CRC method to the state-of-the-art outlier detection methods using outlier detection time, accuracy, error rate and false positive rate evaluation metrics.

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An efficient outlier detection approach on weighted data stream based on minimal rare pattern mining

Cited by 18 publications

References 18 publications

WePaMaDM-Outlier Detection: Weighted Outlier Detection using Pattern Approaches for Mass Data Mining

WePaMaDM-Outlier Detection: Weighted Outlier Detection using Pattern Approaches for Mass Data Mining

A Credit Conflict Detection Model Based on Decision Distance and Probability Matrix

Gaussian Projection Deep Extreme Clustering and Chebyshev Reflective Correlation based Outlier Detection

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