In the case of haze, the flashover voltage of the absolute detector will be reduced, and the probability of pollution flashover will be greatly increased, which is easy to cause the unstable operation of the power sys...
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ISBN:
(纸本)9781450372930
In the case of haze, the flashover voltage of the absolute detector will be reduced, and the probability of pollution flashover will be greatly increased, which is easy to cause the unstable operation of the power system. Through the simulation of haze, we have simulated the sub pollution flashover under different salinity in the high-voltage laboratory, and measured the leakage current and leakage voltage under different pollution conditions. Based on the voltage and current data measured in the laboratory simulation pollution flashover experiment, the K-means clustering model of five kinds of insulator faults is established, and the fault characteristics are summarized and the fault discrimination method is proposed.
As the productivity continues to accelerate, the social era is constantly reforming and progressing. Driven by powerful productivity, the society has entered the era of big data and Internet multimedia today. There is...
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As the productivity continues to accelerate, the social era is constantly reforming and progressing. Driven by powerful productivity, the society has entered the era of big data and Internet multimedia today. There is a lot of big data contained by every industry and job, Before big data analysis, big data classification algorithm must be carried out first. Among many algorithms, the clustering algorithm for high dimensional data stream is relatively convenient and fast. With the increasing of big data, the clustering algorithm for high dimensional data stream should also be innovated to make it more suitable for people to analyze data.
Until now, a lot of clustering algorithms for differential privacy (DP) have been proposed. Practically, there still exist difficulties in implementing these algorithms in a big data platform. In this paper, we propos...
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ISBN:
(纸本)9783030000097;9783030000080
Until now, a lot of clustering algorithms for differential privacy (DP) have been proposed. Practically, there still exist difficulties in implementing these algorithms in a big data platform. In this paper, we proposed a clustering algorithm for privacy preservation on MapReduce. The algorithm is implemented from two aspects. Firstly, the optimized Canopy algorithm is implemented to get the optimal number of clusters and the initial center points on MapReduce. Secondly, the DP K-means algorithm is implemented to get the final clusters on MapReduce. As a result, the proposed algorithm can generate the optimal clustering number that is same with the standard classified data set and can achieve better accuracy of the clusters with the suitable privacy budget epsilon.
Considering the problem of autonomous course-tracking of Unmanned Surface Vessel (USV) with uncertain systems, this paper put forwards a control method based on the clustering algorithm, The designed controller is com...
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ISBN:
(纸本)9789881563958
Considering the problem of autonomous course-tracking of Unmanned Surface Vessel (USV) with uncertain systems, this paper put forwards a control method based on the clustering algorithm, The designed controller is composed of a clustering algorithm controller and a PID controller. Because of the uncertainty of the system, the clustering algorithm analysis controller online learned and classified the expected course to control the rudder, the PID controller adjusts the error. This method solves the problems of low control accuracy and poor control performance of robustness brought by the single system controller, By applying the proposed control method to the control design of USV autonomous course-tracking, the results of the simulation experiment show the effectiveness of the control method.
With the diversification and complexity of power theft methods, the traditional detection methods are difficult to keep up with the changes of power theft methods. Therefore, the detection method of electricity theft ...
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ISBN:
(纸本)9781728133980
With the diversification and complexity of power theft methods, the traditional detection methods are difficult to keep up with the changes of power theft methods. Therefore, the detection method of electricity theft based on data mining technology has become a hot research topic. SOM neural network and K-means clustering algorithm are commonly used clustering methods. SOM neural network can automatically determine the number of clusters, but cannot give accurate clustering information;meanwhile, K-means clustering algorithm has high accuracy but needs to give the initial value in advance. In this paper, on the basis of analyzing the principle of relevant algorithms and the characteristics of electricity data, a method for detecting electricity theft users based on two clustering algorithms is proposed, which can accurately identify electricity theft users through deep mining of abnormal data of electricity users. Theoretical analysis and experimental results show that the method can effectively improve the accuracy of identification of electricity theft, and has certain practicability.
In order to make up for the defect that the traditional spectral clustering algorithm cannot determine the number of clusters and the time-consuming calculation, this paper studies and improves the spectral clustering...
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In order to make up for the defect that the traditional spectral clustering algorithm cannot determine the number of clusters and the time-consuming calculation, this paper studies and improves the spectral clustering algorithm. In complex community networks, the spectral clustering algorithm based on modularity optimization is chosen to find the number of communities. In addition, four types of user attribute information are integrated, and a more reasonable user similarity model is constructed. At the same time, the original non-parallelized spectral clustering algorithm is optimized, and its improved scheme is suitable for the application of distributed computing. Many Hadoop optimization strategies are proposed for virtual community discovery scenarios in large-scale communities. Finally, the experimental results show that the efficiency of the parallelized spectral clustering algorithm is greatly improved, which can be applied to the virtual community discovery in large-scale social networks.
In practice, clustering algorithms usually suffer from the complex structure of the dataset, including data distribution and dimensionality. Meanwhile, the number of clusters, which is required as an input, is usually...
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In practice, clustering algorithms usually suffer from the complex structure of the dataset, including data distribution and dimensionality. Meanwhile, the number of clusters, which is required as an input, is usually unavailable. In this paper, we propose a novel data clustering algorithm: it uses heuristic rules based on k-nearest neighbors chain and does not require the number of clusters as the input parameter. Inspired by the PageRank algorithm, we first use random walk model to measure the importance of data points. Then, on the basis of the important data points, we build a K-Nearest Neighbors Chain (KNNC) to order the k-nearest neighbors by distance and propose two heuristic rules to find the proper number of clusters and initial clusters. The first heuristic rule is the gap of KNNC which reflects the degree of separation of clusters with convex shapes and the second one is the nearest neighbor gap of KNNC which reflects the inner compactness of a cluster. Comprehensive comparison results on synthetic and real datasets indicate that the proposed clustering algorithm can find the proper number of clusters and achieve comparable or even better performance than the popular clustering algorithms.
The density peaks clustering (DPC) is known as an excellent approach to detect some complicated-shaped clusters with high-dimensionality. However, it is not able to detect outliers, hub nodes and boundary nodes, or fo...
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The density peaks clustering (DPC) is known as an excellent approach to detect some complicated-shaped clusters with high-dimensionality. However, it is not able to detect outliers, hub nodes and boundary nodes, or form low-density clusters. Therefore, halo is adopted to improve the performance of DPC in processing low-density nodes. This paper explores the potential reasons for adopting halos instead of low-density nodes, and proposes an improved recognition method on Halo node for Density Peak clustering algorithm (HaloDPC). The proposed HaloDPC has improved the ability to deal with varying densities, irregular shapes, the number of clusters, outlier and hub node detection. This paper presents the advantages of the HaloDPC algorithm on several test cases.
Articulateness and plasticity are two essential attributes that make a graph as an efficient model to real life problems. Nowadays, the attributed graph is received lots of attentions because of usability and effectiv...
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Articulateness and plasticity are two essential attributes that make a graph as an efficient model to real life problems. Nowadays, the attributed graph is received lots of attentions because of usability and effectiveness. In this study, a novel k-Medoid based clustering algorithm, which focuses simultaneously on both structural and contextual aspects using Signal and the weighted Jaccard similarities, are introduced. Two real life data-sets, Political Blogs and DBLP bibliography, are employed in order to evaluate and compare the proposed algorithm with state-of-the-art clustering algorithms. The results show the superiorities of the proposed algorithm in terms of cluster quality metrics.
clustering is an important technique in data mining. The innovative algorithm proposed in this paper obtains clusters by first identifying boundary points as opposed to existing methods that calculate core cluster poi...
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clustering is an important technique in data mining. The innovative algorithm proposed in this paper obtains clusters by first identifying boundary points as opposed to existing methods that calculate core cluster points before expanding to the boundary points. To achieve this, an affine space-based boundary detection algorithm was employed to divide data points into cluster boundary and internal points. A connection matrix was then formed by establishing neighbor relationships between internal and boundary points to perform clustering. Our clustering algorithm with an affine space-based boundary detection algorithm accurately detected clusters in datasets with different densities, shapes, and sizes. The algorithm excelled at dealing with high-dimensional datasets.
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