Research on multi-label classification method of transformer based on DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm

With the improvement of the intelligent level of power grid and the enhancement of the integrated characteristics of power grid, the degree of discretization of massive data of power equipment gradually increases, which brings great challenges to the safe and stable operation of power grid. How to process and analyze data effectively has become an important research content. Transformer is an important electrical equipment, therefore it is of great significance to monitor the operation status of transformer, to construct transformer operation characteristic label system based on multi-source heterogeneous data, and to realize multi-label classification function. In this paper, a transformer multi-label classification method of transformer based on DBSCAN(Density-Based Spatial Clustering of Applications with Noise) clustering algorithm is proposed, which can accurately identify outliers as Noise without input of the number of clustering to be divided, realize the key feature mining of transformer state, and to realize to provide flexible information association and historical data for dispatch and control operators.

Noise Rejection Approaches for Various Rough Set-Based C-Means Clustering

In the field of cluster analysis, rough set-based extensions of hard C-means (HCM; k-means) including rough C-means (RCM), rough set C-means (RSCM), and rough membership C-means (RMCM) are promising approaches for dealing with the certainty, possibility, uncertainty of belonging of object to clusters. Since C-means-type methods are strongly affected by noise, noise clustering approaches have been proposed. In noise clustering approaches, noise objects, which are far from any cluster center, are rejected for robust estimation. In this paper, we introduce noise rejection approaches for rough set-based C-means based on probabilistic memberships and propose noise RCM with membership normalization (NRCM-MN), noise RSCM with membership normalization (NRSCM-MN), and noise RMCM (NRMCM). In addition, visualization demonstration of the cluster boundaries on the two-dimensional plane of the proposed methods is carried out to confirm the characteristics of each method. Furthermore, the clustering performance is verified by numerical experiments using real-world datasets.

Robust Self-Weighted Multi-View Projection Clustering

Many real-world applications involve data collected from different views and with high data dimensionality. Furthermore, multi-view data always has unavoidable noise. Clustering on this kind of high-dimensional and noisy multi-view data remains a challenge due to the curse of dimensionality and ineffective de-noising and integration of multiple views. Aiming at this problem, in this paper, we propose a Robust Self-weighted Multi-view Projection Clustering (RSwMPC) based on ℓ2,1-norm, which can simultaneously reduce dimensionality, suppress noise and learn local structure graph. Then the obtained optimal graph can be directly used for clustering while no further processing is required. In addition, a new method is introduced to automatically learn the optimal weight of each view with no need to generate additional parameters to adjust the weight. Extensive experimental results on different synthetic datasets and real-world datasets demonstrate that the proposed algorithm outperforms other state-of-the-art methods on clustering performance and robustness.