M-FDBSCAN: A multicore density-based uncertain data clustering algorithm

Erdem, Atakan; Gündem, Taflan İmre

doi:10.3906/elk-1202-83

Cited by 12 publications

(4 citation statements)

References 9 publications

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Section: Introductionmentioning

confidence: 99%

“…In this paper, cluster analysis is used, which is a method to classify according to the similarity of different classification features, and singular values are classified to update the training set and as a reference basis for fault diagnosis. [22][23][24][25][26][27][28][29][30][31] Based on the above methods, in order to improve the anti-interference and training set updating ability of the fault diagnosis model of high-voltage common rail system, alpha shapes algorithm and relaxed boundary variables are used to construct the covering sets of various states in the fault diagnosis model training set, Randomly generate the point set that does not belong In order to fully demonstrate the anti-interference ability of the fault diagnosis model of high-voltage common rail system proposed in this paper, the improved EEMD (ensemble empirical mode decomposition) is used to decompose the rail pressure signal of highvoltage common rail system, and the energy method is used to obtain the eigenvector. The fault diagnosis model of high-voltage common rail system is built by SVM (Support Vector Machine) to classify and diagnose the four states.…”

Section: Introductionmentioning

confidence: 99%

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Research on anti-interference and training set updating ability of fault diagnosis model of high voltage common rail system

Shi

Tie-xiong

2022

Advances in Mechanical Engineering

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In the fault diagnosis of high-pressure common rail diesel engine, the problem of low accuracy of fault diagnosis is caused by the inability to classify untrained state problems and training sets. To solve the above problems, an anti-interference classification model for fault diagnosis of high-voltage common rail system based on alpha shapes algorithm and K-means algorithm is proposed in this paper. This model can improve the anti-interference ability and training set updating ability of the fault diagnosis model. Using the anti-interference classification model composed of anti-jamming device and classifier, the samples for fault diagnosis are screened in advance, and the singular values of untrained state and trained state are classified. By constructing an anti-interference classification model composed of anti-interference device and classifier, the fault is diagnosed, and then the dependency of singular values is obtained through cluster analysis, which improve the anti-interference ability and training set updating ability of the fault diagnosis model. By comparing the diagnosis results of the fault diagnosis model with and without anti-interference classifier, we can found that the addition of anti-interference classifier makes the fault diagnosis model of high-voltage common rail system obtain the anti-interference ability to the untrained state and the ability to update the training set of the singular value of the trained state, and can slightly improve the accuracy of fault diagnosis.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on anti-interference and training set updating ability of fault diagnosis model of high voltage common rail system

Shi

Tie-xiong

2022

Advances in Mechanical Engineering

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“…The other advantage of E-MFDBSCAN is the enhanced computational time performance. Because E-MFDBSCAN is a derivation of M-FDBSCAN [18], which is devised for multicore systems, clustering issues can be resolved in each subdataset concurrently by splitting a gene expression dataset into subdatasets.…”

Section: Related Workmentioning

confidence: 99%

“…The uncertain-data-clustering method proposed in [20] and the parallel computation approach proposed in [18] are used in E-MFDBSCAN. According to the density-based clustering approach, there are two main constraints: i) each cluster has at least a number of µ members, and ii) the distance between any two members in a cluster is not larger than ε.…”

Section: E-mfdbscanmentioning

confidence: 99%

E-MFDBSCAN: an evolutionary clustering algorithm for gene expression time series

Erdem¹,

Gündem²

2017

Turk J Elec Eng & Comp Sci

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DNA microarray experiments are frequently used because they have various advantages. However, gene expression data from DNA microarray experiments are noisy, and, consequently, the computations that are based on such noisy data may lack accuracy. In this paper, an evolutionary uncertain data-clustering algorithm, E-MFDBSCAN, and a prediction model using E-MFDBSCAN for uncertain data are proposed. The proposed methodology may be successfully applied to noisy gene expression data. In this methodology, global patterns of time series data can be extracted using our evolutionary clustering approach. These patterns are used to infer future projections. In the proposed methodology, an autoregressive time series function (using these patterns) used to predict the similarities among sets of gene expression clusters is constructed. The algorithms are tested with two different gene expression time series datasets.

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