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Web browsing is a very common way of using the Internet to, among others, read news, do on-line shopping, or search for user generated content such as YouTube or Dailymotion. Traditional evaluations of web surfing focus on objectively measured Quality of Service (QoS) metrics such as loss rate or round-trip times; In this paper, the authors propose to use K-means clustering to share knowledge about the performance of the same web page experienced by different clients. Such technique allows to discover and explain the performance differences among users and identify the root causes for poor performances.
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