RankClus: integrating clustering with ranking for heterogeneous information network analysis

  title={RankClus: integrating clustering with ranking for heterogeneous information network analysis},
  author={Yizhou Sun and Jiawei Han and Peixiang Zhao and Zhijun Yin and Hong Cheng and Tianyi Wu},
As information networks become ubiquitous, extracting knowledge from information networks has become an important task. Both ranking and clustering can provide overall views on information network data, and each has been a hot topic by itself. However, ranking objects globally without considering which clusters they belong to often leads to dumb results, e.g., ranking database and computer architecture conferences together may not make much sense. Similarly, clustering a huge number of objects… CONTINUE READING
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