Perron Cluster Analysis and Its Connection to Graph Partitioning for Noisy Data

@inproceedings{Weber2007PerronCA,
  title={Perron Cluster Analysis and Its Connection to Graph Partitioning for Noisy Data},
  author={Marcus Weber and Wasinee Rungsarityotin and Alexander Schliep},
  year={2007}
}
The problem of clustering data can be formulated as a graph partitioning problem. Spectral methods for obtaining optimal solutions have reveceived a lot of attention recently. We describe Perron Cluster Cluster Analysis (PCCA) and, for the first time, establish a connection to spectral graph partitioning. We show that in our approach a clustering can be efficiently computed using a simple linear map of the eigenvector data. To deal with the prevalent problem of noisy and possibly overlapping… CONTINUE READING
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