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- W1977483251 abstract "Sina microblog has been a very popular social microblog service in recent years. However it's difficult to analyze the network structure of Sina microblog because of the huge amount of users. The emergence of cloud computing gives us a new approach to analyze large-scale social networks. Hadoop is a widely used cloud computing platform, several clustering algorithms such as and Canopy have already been implemented on it. However, the initial cluster centers of are hard to select. Canopy provides a way to choose initial centers, but it is not suitable for very large data sets, and both traditional and Canopy converge very slowly. This paper proposes an improved method to cluster microblog users based on their relationship. We name our method Weight Partitioned Canopy K-means (WPCK), implement it on Hadoop cluster, and test it along with existing methods. Experimental results show that WPCK can reduce the number of iterations by about 1/3 of traditional and Canopy K-means, while their performance are almost the same." @default.
- W1977483251 created "2016-06-24" @default.
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- W1977483251 date "2013-10-01" @default.
- W1977483251 modified "2023-09-27" @default.
- W1977483251 title "Finding User Clusters in Sina Microblog" @default.
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- W1977483251 doi "https://doi.org/10.1109/iscid.2013.107" @default.
- W1977483251 hasPublicationYear "2013" @default.
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