OPE-HCA: an optimal probabilistic estimation approach for hierarchical clustering algorithm

Fan, Jiancong

doi:10.1007/s00521-015-1998-5

Cited by 22 publications

(8 citation statements)

References 30 publications

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“…Considering the weak fault feature extraction in condition monitoring and fault recognition of rolling bearings, Jiao et al (2019) proposed a hierarchical discriminating sparse coding to process the raw signals directly and utilized the hierarchical concept to isolate the interferences. Fan (2019) proposed a new hierarchical clustering algorithm based on the optimal probabilistic estimation approach to address the problem caused by the distance-based measurement and the difficulty of the integration of the cluster, which are the disadvantages of hierarchical clustering algorithms.…”

Section: Hierarchical Clustering Algorithmmentioning

confidence: 99%

Recognition and labeling of faults in wind turbines with a density-based clustering algorithm

Luo

Liu

2021

DTA

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PurposeThe purpose of this paper is to recognize and label the faults in wind turbines with a new density-based clustering algorithm, named contour density scanning clustering (CDSC) algorithm.Design/methodology/approachThe algorithm includes four components: (1) computation of neighborhood density, (2) selection of core and noise data, (3) scanning core data and (4) updating clusters. The proposed algorithm considers the relationship between neighborhood data points according to a contour density scanning strategy.FindingsThe first experiment is conducted with artificial data to validate that the proposed CDSC algorithm is suitable for handling data points with arbitrary shapes. The second experiment with industrial gearbox vibration data is carried out to demonstrate that the time complexity and accuracy of the proposed CDSC algorithm in comparison with other conventional clustering algorithms, including k-means, density-based spatial clustering of applications with noise, density peaking clustering, neighborhood grid clustering, support vector clustering, random forest, core fusion-based density peak clustering, AdaBoost and extreme gradient boosting. The third experiment is conducted with an industrial bearing vibration data set to highlight that the CDSC algorithm can automatically track the emerging fault patterns of bearing in wind turbines over time.Originality/valueData points with different densities are clustered using three strategies: direct density reachability, density reachability and density connectivity. A contours density scanning strategy is proposed to determine whether the data points with the same density belong to one cluster. The proposed CDSC algorithm achieves automatically clustering, which means that the trends of the fault pattern could be tracked.

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Section: Hierarchical Clustering Algorithmmentioning

confidence: 99%

Recognition and labeling of faults in wind turbines with a density-based clustering algorithm

Luo

Liu

2021

DTA

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“…Without loss of generality, we propose to directly select the widely-used hierarchical clustering algorithm (HCA) to perform vessel trajectory clustering in this work. Please refer to (Fan, 2019) for more details on HCA. If the similarity (i.e., distance) computing results are accurate and reliable, it becomes tractable to accurately cluster the vessel trajectories in maritime applications.…”

Section: Extension To Vessel Trajectory Clusteringmentioning

confidence: 99%

An Unsupervised Learning Method with Convolutional Auto-Encoder for Vessel Trajectory Similarity Computation

Liang,

Liu,

et al. 2021

Preprint

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To achieve reliable mining results for massive vessel trajectories, one of the most important challenges is how to efficiently compute the similarities between different vessel trajectories. The computation of vessel trajectory similarity has recently attracted increasing attention in the maritime data mining research community. However, traditional shape-and warping-based methods often suffer from several drawbacks such as high computational cost and sensitivity to unwanted artifacts and non-uniform sampling rates, etc. To eliminate these drawbacks, we propose an unsupervised learning method which automatically extracts low-dimensional features through a convolutional auto-encoder (CAE). In particular, we first generate the informative trajectory images by remapping the raw vessel trajectories into two-dimensional matrices while maintaining the spatio-temporal properties. Based on the massive vessel trajectories collected, the CAE can learn the low-dimensional representations of informative trajectory images in an unsupervised manner. The trajectory similarity is finally equivalent to efficiently computing the similarities between the learned low-dimensional features, which strongly correlate with the raw vessel trajectories. Comprehensive experiments on realistic data sets have demonstrated that the proposed method largely outperforms traditional trajectory similarity computation methods in terms of efficiency and effectiveness. The high-quality trajectory clustering performance could also be guaranteed according to the CAE-based trajectory similarity computation results.

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“…Fan et al proposed an inhibitory fuzzy C-means clustering algorithm. 2 The literature [4][5][6] gave the other variations of the fuzzy C-means algorithm. At the same time, the combination of rough set theory and clustering algorithm gradually emerged, and fuzzy clustering algorithm based on rough set was proposed.…”

Section: Related Workmentioning

confidence: 99%

“…Fan et al proposed a rough set clustering algorithm which can solve the boundary uncertainty problem. 6,[27][28][29][30][31] The rough set theory is often used to solve the local convergence problem. 30,39 For the problem of batch complexity caused by the batch and dynamic incremental clustering, Li et al proposed a dynamic incremental C-means algorithm based on rough set theory, which greatly reduced the complexity of the cluster.…”

Section: Related Workmentioning

confidence: 99%

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A novel fuzzy clustering algorithm based on rough set and inhibitive factor

Tang

Wang

et al. 2020

Concurrency and Computation

Self Cite

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As an important data analysis method, rough set theory can be used to analyze data with uncertainties. Rough set is able to acquire knowledge by the indistinguishable relationship among data objects without any prior knowledge. Rough set theory provides a new theoretical means for solving soft computing problems and has a wide application space in data mining. Meanwhile, the fuzzy C-means clustering algorithm is sensitive to noisy points and has low convergence speed. In order to deal with the above problems, a novel fuzzy clustering algorithm based on rough set and inhibitive factor is proposed. According to the related concepts of rough set theory, the membership model of fuzzy C-means algorithm is redefined. Besides, an inhibitive factor is set to improve the convergence speed of the algorithm under the premise of guaranteeing the clustering effect. K E Y W O R D S fuzzy clustering, inhibitive factor, membership degree, rough set 1 INTRODUCTION There are many cluster analysis approaches in the data clustering field. From the view of the membership degrees of data sample, cluster analysis can be divided into two categories. One is the hard clustering analysis, the other is the soft clustering analysis, also known as fuzzy clustering analysis. 1 Hard clustering is a kind of strict division, which aims to divide every data sample into a specific cluster. Its division mechanism is very clear with only two membership degrees: 0 and 1, which means a sample can only belong to a certain class or not at all. This clustering method is too strict to deal with some data at the boundary, which could easily cause errors. Soft clustering is more tolerant for the division of data and divides them into different clusters according to their membership degree. The traditional fuzzy clustering method is sensitive to noise data, and the accuracy of clustering is often not good enough when dealing with complex data sets. Although some data objects are divided into a certain cluster, it is not obvious that they belong to that one. Rough set theory can deal with the fuzzy and uncertain problems. The idea of inhibitive factor comes from the suppressed fuzzy C-means clustering algorithm (S-FCM). 2 The essence of S-FCM is prizing the biggest membership and suppressing the others. By choosing the reasonable a parameter, S-FCM converges faster than FCM under the precondition of not decreasing clustering performance. In this article, the related work about fuzzy clustering algorithm based on rough set is overviewed in Section 2. In Section 3 the related concepts of rough set theory and fuzzy method are introduced. Section 4 designs a new calculation method of fuzzy membership degree. At the same time, an inhibitive factor is used to improve the convergence speed of the algorithm, and then a fuzzy clustering algorithm based on rough set and the inhibitive factor is proposed. The experimental results and analysis are given in Section 6. Section 6 gives the conclusion of this article.

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OPE-HCA: an optimal probabilistic estimation approach for hierarchical clustering algorithm

Cited by 22 publications

References 30 publications

Recognition and labeling of faults in wind turbines with a density-based clustering algorithm

Recognition and labeling of faults in wind turbines with a density-based clustering algorithm

An Unsupervised Learning Method with Convolutional Auto-Encoder for Vessel Trajectory Similarity Computation

A novel fuzzy clustering algorithm based on rough set and inhibitive factor

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