Detection of local and clustered outliers based on the density–distance decision graph

Li, Kangsheng; Gao, Xin; Jia, Xin; Xue, Bing; Fu, Shiyuan; Liu, Zhiyu; Huang, Xu; Huang, Zijian

doi:10.1016/j.engappai.2022.104719

Cited by 19 publications

(3 citation statements)

References 13 publications

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“…This study applied a mix of unsupervised and supervised algorithms for anomaly detection. Some of these were adopted by previous research studies such as, isolation tree/forest (Liu et al , 20028), nearest neighbor (Radovanović et al , 2014), clustering (li et al , 2022), statistical methods (Zimek and Filzmoser, 2018). In this study, more emphasis is given on unsupervised algorithms, although supervised learning may also be applicable because learning the expected behavior is considerably easier than learning the types of anomalies (Samariya and Thakkar, 2021).…”

Section: Discussionmentioning

confidence: 99%

Anomaly detection in the course evaluation process: a learning analytics–based approach

Vaidya

Sharma

2023

ITSE

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Purpose Course evaluations are formative and are used to evaluate learnings of the students for a course. Anomalies in the evaluation process can lead to a faulty educational outcome. Learning analytics and educational data mining provide a set of techniques that can be conveniently applied to extensive data collected as part of the evaluation process to ensure remedial actions. This study aims to conduct an experimental research to detect anomalies in the evaluation methods. Design/methodology/approach Experimental research is conducted with scientific approach and design. The researchers categorized anomaly into three categories, namely, an anomaly in criteria assessment, subject anomaly and anomaly in subject marks allocation. The different anomaly detection algorithms are used to educate data through the software R, and the results are summarized in the tables. Findings The data points occurring in all algorithms are finally detected as an anomaly. The anomaly identifies the data points that deviate from the data set’s normal behavior. The subject which is consistently identified as anomalous by the different techniques is marked as an anomaly in evaluation. After identification, one can drill down to more details into the title of anomalies in the evaluation criteria. Originality/value This paper proposes an analytical model for the course evaluation process and demonstrates the use of actionable analytics to detect anomalies in the evaluation process.

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Section: Discussionmentioning

confidence: 99%

Anomaly detection in the course evaluation process: a learning analytics–based approach

Vaidya

Sharma

2023

ITSE

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“…As is known, samples from the lowdensity region are more valuable for constructing the classifier, since the ideal classification hyperplane often passes through the sparse data region with the lowest density in the feature space. [17] Local reachable density (LRD) [18,19] and local outlier factor (LOF) [20,21] can effectively evaluate the density of samples. LRD represents the density of samples based on the K-distance neighbourhood, [22] and LOF represents the average ratios of the LRD of the sample neighbourhood points to the LRD of the sample.…”

Section: Introductionmentioning

confidence: 99%

A novel density ratio‐based batch active learning fault diagnosis method integrated with adaptive Laplacian graph trimming

Jiang

Zhu

et al. 2023

Can J Chem Eng

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In actual industrial processes, although a large number of original data are easy to obtain, only a few samples are effectively labelled, which is insufficient to construct a supervised fault diagnostic model. Facing the industrial demand of fault diagnosis, in this paper, a novel density ratio (DR)‐based batch active learning (BAL) fault diagnosis method integrated with adaptive Laplacian graph trimming (ALGT) method is proposed. First, under the active learning framework, a new index DR‐based on local reachability density (LRD) is proposed to search the low density and high uncertainty samples, in which the local outliers factor (LOF) is used to search the samples in low density region and the ratio of LRD and intra‐class LRD is calculated to search the samples with high uncertainty. Second, the samples are selected and manually labelled in batches according to the proposed index DR, and the labelled data set and the unlabelled data set are updated and reconstructed. Third, based on the reconstructed labelled dataset and remaining unlabelled dataset, a semi‐supervised classifier ALGT is constructed for fault diagnosis. In ALGT, the Laplacian weighted graph is initialized and iteratively optimized by ALGT. Finally, the proposed DR‐based BAL‐ALGT (DRBAL‐ALGT) fault diagnosis method is verified by the Tennessee Eastman process (TEP) and applied to grid‐connected photovoltaic systems (GPVS). The experimental results show that the proposed DRBAL‐ALGT method can achieve higher accuracy for fault diagnosis.

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“…Researchers have recently proposed various methods for diagnosing abnormal process parameters, including clustering-based methods, density-based methods, data-driven methods, and expert system methods [6][7][8][9]. The clustering-based approach involves clustering the data of parameters into multiple clusters, where the cluster with the least data points is considered to be the abnormal cluster.…”

Section: Introductionmentioning

confidence: 99%

An Expert System Based on Data Mining for a Trend Diagnosis of Process Parameters

Wang,

Zhang

et al. 2023

Processes

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In order to diagnose abnormal trends in the process parameters of industrial production, the Expert System based on rolling data Kernel Principal Component Analysis (ES-KPCA) and Support Vector Data Description (ES-SVDD) are proposed in this paper. The expert system is capable of identifying large-scale trend changes and abnormal fluctuations in process parameters using data mining techniques, subsequently triggering timely alarms. The system consists of a rule-based assessment of process parameter stability to evaluate whether the process parameters are stable. Also, when the parameters are unstable, the rolling data-based KPCA and SVDD methods are used to diagnose abnormal trends. ES-KPCA and ES-SVDD methods require adjusting seven threshold parameters during the offline parameter adjustment phase. The system obtains the adjusted parameters and performs a real-time diagnosis of process parameters based on the set diagnosis interval during the online diagnosis phase. The ES-KPCA and ES-SVDD methods emphasize the real-time alarms and the first alarm of process parameter abnormal trends, respectively. Finally, the system validates the experimental data from UniSim simulation and a chemical plant. The results show that the expert system has an outstanding diagnostic performance for abnormal trends in process parameters.

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Detection of local and clustered outliers based on the density–distance decision graph

Cited by 19 publications

References 13 publications

Anomaly detection in the course evaluation process: a learning analytics–based approach

Anomaly detection in the course evaluation process: a learning analytics–based approach

A novel density ratio‐based batch active learning fault diagnosis method integrated with adaptive Laplacian graph trimming

An Expert System Based on Data Mining for a Trend Diagnosis of Process Parameters

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