Open AccessA semi-supervised clustering algorithm for real network traffic with concept drift
Author(s) -
Hua Qu,
Jinchuan Jie,
Jihong Zhao,
Yanpeng Zhang
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/569/5/052045
Subject(s) - cluster analysis , computer science , data stream clustering , canopy clustering algorithm , data mining , artificial intelligence , correlation clustering , cure data clustering algorithm , machine learning , algorithm
Traffic classification has been widely applied for networking. Previous works paid much attention to static network traffic. In this paper, we propose a new strategy for the semi-supervised clustering algorithm to deal the concept drift in a dynamic network, as well as updating the model incrementally. Moreover, our algorithm can find new clusters and reduce the impact of noises. The results of simulation demonstrate the effectiveness of semi-supervised clustering algorithm.