Abstract: In order to improving the network quality of service by mining useful model from multi-source and complicated network performance data, a clustering analysis algorithm for network performance monitoring data based on ontology. The semantic description method of network performance monitoring data is described, then a similarity measurement model of network performance data based on semantic description and property data is proposed, and an NJW spectral clustering algorithm based on improved k-means algorithm is given. The experiments based on the UCI data sets and the performance monitoring data on a campus network shows that the proposed algorithm has a higher clustering accuracy and differentiation than the comparative algorithms.

Key words: ontology, semantic description, semantic similarity, spectral clustering, network performance monitoring

摘要： 为了从多源复杂的网络性能数据中挖掘有用模式以提高网络服务质量,研究了基于本体的网络性能监测数据聚类分析方法。阐述了网络性能监测数据的语义描述方法，提出基于语义和属性数据相融合的网络性能数据相似性度量模型,并给出基于改进k-means的NJW谱聚类算法。通过在UCI数据集和校园网性能监测数据集上的实验表明, 本文所提方法较相关比对方法具有更高的聚类准确性和区分度。

关键词: 本体, 语义描述, 语义相似度, 谱聚类, 网络性能监测