A Traffic Classification Method with Spectral Clustering in SDN

2016 ◽  
Author(s):  
Peng Xiao ◽  
Na Liu ◽  
Yuanyuan Li ◽  
Ying Lu ◽  
Xiao-Jun Tang ◽  
...  
Keyword(s):  
Spectral Clustering ◽  
Classification Method ◽  
Traffic Classification

scholarly journals A Novel Unsupervised Classification Method for Sandy Land Using Fully Polarimetric SAR Data

2021 ◽  
Vol 13 (3) ◽  
pp. 355
Author(s):  
Weixian Tan ◽  
Borong Sun ◽  
Chenyu Xiao ◽  
Pingping Huang ◽  
Wei Xu ◽  
...  
Keyword(s):  
Spectral Clustering ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Feature Vector ◽  
Unsupervised Classification ◽  
Classification Method ◽  
Sandy Land ◽  
Classification Methods ◽  
The Many ◽  
Representative Points

Classification based on polarimetric synthetic aperture radar (PolSAR) images is an emerging technology, and recent years have seen the introduction of various classification methods that have been proven to be effective to identify typical features of many terrain types. Among the many regions of the study, the Hunshandake Sandy Land in Inner Mongolia, China stands out for its vast area of sandy land, variety of ground objects, and intricate structure, with more irregular characteristics than conventional land cover. Accounting for the particular surface features of the Hunshandake Sandy Land, an unsupervised classification method based on new decomposition and large-scale spectral clustering with superpixels (ND-LSC) is proposed in this study. Firstly, the polarization scattering parameters are extracted through a new decomposition, rather than other decomposition approaches, which gives rise to more accurate feature vector estimate. Secondly, a large-scale spectral clustering is applied as appropriate to meet the massive land and complex terrain. More specifically, this involves a beginning sub-step of superpixels generation via the Adaptive Simple Linear Iterative Clustering (ASLIC) algorithm when the feature vector combined with the spatial coordinate information are employed as input, and subsequently a sub-step of representative points selection as well as bipartite graph formation, followed by the spectral clustering algorithm to complete the classification task. Finally, testing and analysis are conducted on the RADARSAT-2 fully PolSAR dataset acquired over the Hunshandake Sandy Land in 2016. Both qualitative and quantitative experiments compared with several classification methods are conducted to show that proposed method can significantly improve performance on classification.

A New Network Traffic Classification Method Based on Optimized Hadamard Matrix and ECOC-SVM

2014 ◽  
Vol 989-994 ◽  
pp. 1895-1900
Author(s):  
Hong Zhi Wang ◽  
Li Hui Yan
Keyword(s):  
Network Traffic ◽  
Classification Accuracy ◽  
Hadamard Matrix ◽  
Classification Method ◽  
Traffic Classification ◽  
Classification Methods ◽  
Computational Burden ◽  
Network Traffic Classification ◽  
Encrypted Traffic

The traditional network traffic classification methods have many shortcomings, the classification accuracy is not high, the encrypted traffic cannot be analyzed, and the computational burden is usually large. To overcome above problems, this paper presents a new network traffic classification method based on optimized Hadamard matrix and ECOC. Through restructuring the Hadamard matrix and erasing the interference rows and columns, the ECOC table is optimized while eliminating SVM sample imbalance, and the error correcting ability for classification is reserved. The experiments results show that the proposed method outperform in network traffic classification and improve the classification accuracy.

A Traffic-Classification Method Using the Correlation of the Network Flow

2017 ◽  
Vol 44 (4) ◽  
pp. 433-438
Author(s):  
YoungHoon Goo ◽  
Sungho Lee ◽  
Kyuseok Shim ◽  
Baraka D. Sija ◽  
MyungSup Kim
Keyword(s):  
Network Flow ◽  
Classification Method ◽  
Traffic Classification
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