Enhanced synchronization-inspired clustering for high-dimensional data

Chen, Lei; Guo, Qinghua; Liu, Zhaohua; Zhang, Shiwen; Zhang, Hongqiang

doi:10.1007/s40747-020-00191-y

Cited by 15 publications

(5 citation statements)

References 27 publications

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“…In recent years, some classic clustering algorithms have been developed and expanded, such as modified k-means clustering algorithm [13], density spatial clustering algorithm [14], K-nearest neighbor decision clustering algorithm [15], density deviation multi-peaks automatic clustering algorithm [16], and synchronization-inspired clustering algorithm [17]. The classic clustering algorithms are effective when the data structures are the combination of simple form and the features are representative.…”

Section: Classic Clusteringmentioning

confidence: 99%

Flight risk evaluation based on flight state deep clustering network

Wang

Pei

et al. 2023

Complex Intell. Syst.

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Flight risk evaluation based on data-driven approach is an essential topic of aviation safety management. Existing risk analysis methods ignore the coupling and time-variant characteristics of flight parameters, and cannot accurately establish the mapping relationship between flight state and loss-of-control risk. To deal with the problem, a flight state deep clustering network (FSDCN) model was proposed to mine latent loss-of-control risk information implicating in raw flight parameters. FSDCN integrates the feature extraction and clustering into an end-to-end deep hybrid network to extract latent risk features from multivariate time-series flight parameters and cluster them. In the FSDCN model, a sequential multi-attention encoder–decoder network is designed to extract embedded risk features, and the feature clustering layer is designed to iteratively refine clustering effects and feature extraction. Besides, a loss-of-control classifier is added to optimize the risk feature vector expression and ensure sufficient dividing feature for facilitate clustering. The multi-task joint learning strategy is adopted to improve the clustering performance of the model further. According to extracted risk features and similarity metrics, the optimal clusters number of flight states is set as 5. Comparative experiments show that FSDCN significantly performs better than other clustering models with performance percentage error below $$6\%$$ 6 % . Through statistical analysis of clustering results, the risk level is quantified for each cluster. Three high-difficulty maneuver cases are presented to demonstrate FSDCN for flight risk evaluation. The flight parameter sequences of the maneuver cases are input into the well-trained FSDCN to obtain the risk prediction results. The spatiotemporal distribution characteristics of the risk-quantized results are consistent with flight parameters over-limit situations, which demonstrates the effectiveness of FSDCN on clustering flight states. The experimental results on flight maneuver cases show that FSDCN can find potential loss-of-control risk features according to multivariate time-series flight data and provide support for in-flight risk warnings.

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Section: Classic Clusteringmentioning

confidence: 99%

Flight risk evaluation based on flight state deep clustering network

Wang

Pei

et al. 2023

Complex Intell. Syst.

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“…Manuscript received: 2023-02-21; revised: 2023-05-12; accepted: 2023-05-15 is responsible for converting a strongly coupled HN from a high-dimensional space to a discrete node vector in a low-dimensional space, and these vectors can preserve the rich semantic and structural information of this HN to the maximum possible extent [2,3] . HN representation learning can be used to mine valuable information hidden in an HN, and it has been proved to be of considerable assistance to many downstream network analysis tasks, such as node classification, link prediction, and visualization [4][5][6] .…”

Section: Introductionmentioning

confidence: 99%

Metarelation2vec: A Metapath-Free Scalable Representation Learning Model for Heterogeneous Networks

Chen,

Li,

Lei

et al. 2024

Tsinghua Sci. Technol.

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Metapaths with specific complex semantics are critical to learning diverse semantic and structural information of heterogeneous networks (HNs) for most of the existing representation learning models. However, any metapaths consisting of multiple, simple metarelations must be driven by domain experts. These sensitive, expensive, and limited metapaths severely reduce the flexibility and scalability of the existing models. A metapathfree, scalable representation learning model, called Metarelation2vec, is proposed for HNs with biased joint learning of all metarelations in a bid to address this problem. Specifically, a metarelation-aware, biased walk strategy is first designed to obtain better training samples by using autogenerating cooperation probabilities for all metarelations rather than using expert-given metapaths. Thereafter, grouped nodes by the type, a common and shallow skip-gram model is used to separately learn structural proximity for each node type. Next, grouped links by the type, a novel and shallow model is used to separately learn the semantic proximity for each link type. Finally, supervised by the cooperation probabilities of all meta-words, the biased training samples are thrown into the shallow models to jointly learn the structural and semantic information in the HNs, ensuring the accuracy and scalability of the models.Extensive experimental results on three tasks and four open datasets demonstrate the advantages of our proposed model.

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“…It represents the mainstream methods used for reactive voltage partitioning. However, certain hierarchical clustering-based methods [13][14][15] primarily focus on network topology, lacking a comprehensive integration of the electrical characteristics and actual operating conditions of the power grid. Additionally, partitioning clustering-based methods [16,17] may encounter difficulties when dealing with datasets containing non-convex cluster shapes.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Density Peak-Based Power Grid Reactive Voltage Partitioning

Deng,

Liu,

Liu

et al. 2023

Energies

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Clustering-based reactive voltage partitioning is successful in reducing grid cascading faults, by using clustering methods to categorize different power-consuming entities in the power grid into distinct regions. In reality, each power-consuming entity has different electrical characteristics. Additionally, due to the irregular and uneven distribution of the population, the distribution of electricity consumption is also irregular and uneven. However, the existing method neglects the electrical difference among each entity and the irregular and uneven density distribution of electricity consumption, resulting in poor accuracy and adaptability of these methods. To address these problems, an enhanced density peak model-based power grid reactive voltage partitioning method is proposed in this paper, called EDPVP. First, the power grid is modeled as a weighted reactive network to consider entity electrical differences. Second, the novel local density and density following distance are designed to enhance the density peak model to address the problem that the traditional density peak model cannot adapt to weighted networks. Finally, the enhanced density peak model is further equipped with an optimized cluster centers selection strategy and an updated remaining node assignment strategy, to better identify irregular and uneven density distribution of electricity consumption, and to achieve fast and accurate reactive voltage partition. Experiments on two real power grids demonstrate the effectiveness of the EDPVP.

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Enhanced synchronization-inspired clustering for high-dimensional data

Cited by 15 publications

References 27 publications

Flight risk evaluation based on flight state deep clustering network

Flight risk evaluation based on flight state deep clustering network

Metarelation2vec: A Metapath-Free Scalable Representation Learning Model for Heterogeneous Networks

Enhanced Density Peak-Based Power Grid Reactive Voltage Partitioning

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