ALID: Scalable Dominant Cluster Detection

@article{Chu2015ALIDSD,
  title={ALID: Scalable Dominant Cluster Detection},
  author={Lingyang Chu and Shuhui Wang and Siyuan Liu and Qingming Huang and Jian Pei},
  journal={PVLDB},
  year={2015},
  volume={8},
  pages={826-837}
}
Detecting dominant clusters is important in many analytic applications. The state-of-the-art methods find dense subgraphs on the affinity graph as dominant clusters. However, the time and space complexities of those methods are dominated by the construction of affinity graph, which is quadratic with respect to the number of data points, and thus are impractical on large data sets. To tackle the challenge, in this paper, we apply Evolutionary Game Theory (EGT) and develop a scalable algorithm… CONTINUE READING
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