Identifying robust communities and multi-community nodes by combining top-down and bottom-up approaches to clustering

@inproceedings{Gaiteri2015IdentifyingRC,
  title={Identifying robust communities and multi-community nodes by combining top-down and bottom-up approaches to clustering},
  author={Christopher Gaiteri and Mingming Chen and Boleslaw K. Szymanski and Konstantin Kuzmin and Jierui Xie and Changkyu Lee and Timothy Blanche and Elias Chaibub Neto and Su-Chun Huang and Thomas J. Grabowski and Tara M. Madhyastha and Vitalina Komashko},
  booktitle={Scientific reports},
  year={2015}
}
Biological functions are carried out by groups of interacting molecules, cells or tissues, known as communities. Membership in these communities may overlap when biological components are involved in multiple functions. However, traditional clustering methods detect non-overlapping communities. These detected communities may also be unstable and difficult to replicate, because traditional methods are sensitive to noise and parameter settings. These aspects of traditional clustering methods… CONTINUE READING
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A (2014) NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set

  • M Charrad, N Ghazzali, V Boiteau, Niknafs
  • Journal of Statistical Software
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