NDLI: QoS Prediction of Web Services Based on Two-Phase K-Means Clustering

Content Provider	IEEE Xplore Digital Library
Author	Chen Wu Weiwei Qiu Zibin Zheng Xinyu Wang Xiaohu Yang
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong, Hong Kong, China (Zibin Zheng) \|\| Coll. of Comput. Sci. & Technol., Zhejiang Univ., Hangzhou, China (Chen Wu; Weiwei Qiu; Xinyu Wang; Xiaohu Yang)
Abstract	QoS prediction for Web services is a hot research problem in the field of services computing. As one of the most important methods for QoS prediction, Collaborative Filtering (CF) makes prediction based on the historical QoS data contributed by similar users and services. The key issue in this process is to detect the unreliable data offered by untrustworthy users, which has attracted limited attentions so far. The utilization of unreliable data decreases the prediction accuracy greatly. In this paper, we propose a novel credibility-aware QoS prediction method (named CAP) to address this problem. Our method first employs two-phase K-means clustering to identify the untrustworthy users, which clusters QoS values for untrustworthy index calculation in the first phase and clusters users according to their index in the second phase, and then predicts the missing QoS value based on the credible clustering information. The evaluation results demonstrate that CAP provides considerable improvement on the prediction accuracy compared with other approaches and is robust against various percentages of untrustworthy users.
Starting Page	161
Ending Page	168
File Size	230874
Page Count	8
File Format	PDF
e-ISBN	9781467372725
DOI	10.1109/ICWS.2015.31
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Quality of service Clustering algorithms Web services Indexes Prediction algorithms Accuracy Complexity theory QoS prediction K-means clustering collaborative filtering
Content Type	Text
Resource Type	Article

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