Research on Spectral Clustering Algorithm for Network Communication Big Data Based on Wavelet Analysis

Author(s):  
Zhichao Zeng
2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Guangshun Li ◽  
Shuzhen Xu ◽  
Junhua Wu ◽  
Heng Ding

With the development of Internet of Things (IoT), the massive data generated by it forms big data, and the complexity of dealing with big data brings challenges to resource scheduling in edge computing. In order to solve the problem of resource scheduling and improve the satisfaction of users in edge computing environment, we propose a user-oriented improved spectral clustering scheduling algorithm (ISCM) in this paper. Based on the improved k-means algorithm, the ISCM algorithm solves the problem that the clustering result is sensitive to the initial value and realizes the reclustering, which makes the obtained clustering results more stable. Finally, the edge computing resource scheduling scheme is obtained based on the clustering results. The experimental results show that the resource scheduling scheme based on improved spectral clustering algorithm is superior to traditional spectral clustering algorithm in edge computing environment.


Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 596
Author(s):  
Krishna Kumar Sharma ◽  
Ayan Seal ◽  
Enrique Herrera-Viedma ◽  
Ondrej Krejcar

Calculating and monitoring customer churn metrics is important for companies to retain customers and earn more profit in business. In this study, a churn prediction framework is developed by modified spectral clustering (SC). However, the similarity measure plays an imperative role in clustering for predicting churn with better accuracy by analyzing industrial data. The linear Euclidean distance in the traditional SC is replaced by the non-linear S-distance (Sd). The Sd is deduced from the concept of S-divergence (SD). Several characteristics of Sd are discussed in this work. Assays are conducted to endorse the proposed clustering algorithm on four synthetics, eight UCI, two industrial databases and one telecommunications database related to customer churn. Three existing clustering algorithms—k-means, density-based spatial clustering of applications with noise and conventional SC—are also implemented on the above-mentioned 15 databases. The empirical outcomes show that the proposed clustering algorithm beats three existing clustering algorithms in terms of its Jaccard index, f-score, recall, precision and accuracy. Finally, we also test the significance of the clustering results by the Wilcoxon’s signed-rank test, Wilcoxon’s rank-sum test, and sign tests. The relative study shows that the outcomes of the proposed algorithm are interesting, especially in the case of clusters of arbitrary shape.


2011 ◽  
Vol 121-126 ◽  
pp. 2372-2376
Author(s):  
Dan Dan Wang ◽  
Yu Zhou ◽  
Qing Wei Ye ◽  
Xiao Dong Wang

The mode peaks in frequency domain of vibration signal are strongly interfered by strong noise, causing the inaccuracy mode parameters. According to this situation, this paper comes up with the thought of mode-peak segmentation based on the spectral clustering algorithm. First, according to the concept of wave packet, the amplitude-frequency of vibration signal is divided into wave packets. Taking each wave packet as a sample of clustering algorithm, the spectral clustering algorithm is used to classify these wave packets. The amplitude-frequency curve of a mode peak becomes a big wave packet in macroscopic. The experiment to simulation signals indicates that this spectral clustering algorithm could accord with the macroscopic observation of mode segmentation effectively, and has outstanding performance especially in strong noise.


2014 ◽  
Vol 687-691 ◽  
pp. 1350-1353
Author(s):  
Li Li Fu ◽  
Yong Li Liu ◽  
Li Jing Hao

Spectral clustering algorithm is a kind of clustering algorithm based on spectral graph theory. As spectral clustering has deep theoretical foundation as well as the advantage in dealing with non-convex distribution, it has received much attention in machine learning and data mining areas. The algorithm is easy to implement, and outperforms traditional clustering algorithms such as K-means algorithm. This paper aims to give some intuitions on spectral clustering. We describe different graph partition criteria, the definition of spectral clustering, and clustering steps, etc. Finally, in order to solve the disadvantage of spectral clustering, some improvements are introduced briefly.


2019 ◽  
Vol 92 (2) ◽  
pp. 213-221 ◽  
Author(s):  
S.W. Soh ◽  
Z.W. Zhong

Purpose Given the ever-growing air travel industry, there is an increasing strain on the systems that provide safe flights. Different methods have to be adopted to help to cope with the increasing demand, especially in Southeast Asia. The purpose of this study is to sectorise one existing airspace to better manage sector workloads. Design/methodology/approach Cambodia’s airspace was chosen for this study because it had only one sector and it was quickly approaching its limit. In this paper, after characterising the airspace, it was first bi-partitioned using the spectral clustering algorithm. The weights of the resulting subgraphs were then balanced through a weight-balancing algorithm. Also, a post-processing algorithm established the sector boundary to be drawn. The method was first carried out on one test airspace. Following the successful sectorisation of the test airspace, the actual Cambodian airspace was sectorised. The resulting two new sectors were then calculated to be able to last for approximately five years before they would reach their capacity. Hence, a further sectorisation was carried out. This resulted in four sectors, which were projected to last more than 10 years. Findings The method produced satisfactory results. The methodology presented is proven to be effective in achieving the sectorisation. The workloads of the new sectors obtained are balanced, and the sector boundaries are at least 15 km away from the air routes and nodes. The methodology is also general and can be applied to different scenarios. This means that applications to other airspace in the region are possible, which can further help to increase the safety, efficiency and capacity of the air traffic movement in this region. Originality/value This paper presents one of the approaches for airspace sector designs. The problems are clearly presented with references. The authors discuss the advantages and disadvantages of subdividing airspace and the need to sectorise Cambodia’s airspace, and present a method to solve the sectorisation problem. It is very precious to apply methodologies and algorithms to real cases. The presented method offers significant advantages such as the ease of implementation and efficiency. The problems can easily be solved using standard linear algebra algorithms. Instead of looking at the airspace as a whole, and generating new sector boundaries, our algorithm uses current sector boundaries and bisects them. Moreover, only sectors that require sectorisation would be affected. This algorithm has the advantage of maintaining the current sector boundaries to prevent radical changes to daily operations. The Voronoi diagram used in this work does not generate polygonal cells. It instead calculates the area based on pixels. The advantage of doing this is that it offers higher flexibility. Also, the sector boundary is generated based on straight lines calculated by joining the midpoints of links. This is simple and ensures that sections of the sector boundary are made up of straight, distinct lines. The authors also discuss the problems of the method and presented solutions to them.


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