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Research on degree of video completion of Internet videos with clustering algorithms

机译:聚类算法研究互联网视频的视频完成度

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Clustering algorithms are some of the most important algorithms used to describe data attributes and very effective ways to mine and analyze big data set. In this paper, the whole description of vector space of Internet users is acquired by clustering and analyzing user behavior data set. Moreover, all users are divided into different clusters according to KPI which is to correlate different users in terms of their degree of video completion. Our research shows that KPI of total length correlates degree of completion better than other KPIs. This KPI is drastically negatively correlated with user degree of video completion. Finally, we compare the accuracy and efficiency of three different algorithms which we used to cluster our research data in this paper.
机译:聚类算法是一些最重要的算法,用于描述数据属性以及挖掘和分析大数据集的非常有效的方法。本文通过聚类和分析用户行为数据集,获得了互联网用户向量空间的整体描述。此外,根据KPI将所有用户划分为不同的群集,这将使不同用户的视频完成度相关联。我们的研究表明,总长度的KPI与其他KPI的关联程度更好。该KPI与用户的视频完成度显着负相关。最后,我们比较了本文用来聚类研究数据的三种不同算法的准确性和效率。

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