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Statistics-based Internet traffic classification using machine learning techniques has attracted extensive research interest lately, because of the increasing ineffectiveness of traditional port-based and payload-based approaches. In particular, unsupervised learning, that is, traffic clustering, is very important in real-life applications, where labeled training data are difficult to obtain and new...
The recent years have seen extensive work on statistics-based network traffic classification using machine learning (ML) techniques. In the particular scenario of learning from unlabeled traffic data, some classic unsupervised clustering algorithms (e.g. K-Means and EM) have been applied but the reported results are unsatisfactory in terms of low accuracy. This paper presents a novel approach for...
Immune Genetic Algorithm-based Load Balancing (IGALB) was proposed to improve the efficiency of search quality and the poor performance of local search in the Simple Genetic Algorithm-based Load Balancing (SGALB). This algorithm ensured the diversity of population and overcame the SGALB premature convergence by carrying out the affinity and concentration calculations. Meanwhile under certain conditions...
In order to precisely procure the Chinese person information on the web, especially distinguish from the namesake, this paper propose a clustering algorithm based on latent semantic model. It establishes for every document a latent semantic model of sentence-word matrix based on central distance, central segment, document length, etc, by building the central word library of person attributes. It clusters...
This paper presents a new method for the mining the hottest topics on Chinese Web page which is based on the improved k-means partitioning algorithm. The dictionary applied to word segmentation is reduced by deleting words is which are useless for clustering, and the dictionary tree is created to be applied to word segmentation. Then the speed of word segmentation is improved. Correspondence between...
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