In order to improve scalability and noise robustness,a QoS prediction method based on data feature perception potential factor is proposed.The dense potential factors were extracted from the original sparse data of QoS to detect the neighborhood and noise of users and services.The density peak clustering method was introduced in the modeling process to realize the simultaneous detection of QoS data neighborhood and noise.The given QoS data could be accurately expressed and the high-precision prediction of unknown data could be realized.Experimental results on two QoS data sets generated by real Web services show that the proposed method can effectively improve the prediction accuracy and robustness.
关键词
数据特征感知/潜在因子/服务质量/密度峰值聚类
Key words
Data feature perception/Potential factors/Service quality/Density peak clustering
维普
万方数据