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

  title={Perron Cluster Analysis and Its Connection to Graph Partitioning for Noisy Data},
  author={Marcus Weber and Wasinee Rungsarityotin and Alexander Schliep},
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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Publications referenced by this paper.
Showing 1-10 of 30 references

Robust Perron Cluster Analysis in Conformation Dynamics

  • P. Deuflhard, M. Weber
  • Technical Report ZIB 03-19, Zuse Institute Berlin…
  • 2003
Highly Influential
11 Excerpts

Characterization of transition states in conformational dynamics using Fuzzy sets

  • M. Weber, T. Galliat
  • Technical Report Report 02–12,
  • 2002
Highly Influential
4 Excerpts

Clustering by using a simplex structure

  • M. Weber
  • Technical report,
  • 2004
3 Excerpts

FCM, a fuzzy map clustering algorithm for microarray data analysis

  • L. M. Fu, E. Medico
  • Bioinformatics ITalian Society Meeting (BITS04),
  • 2004
1 Excerpt

Schu ̈tte. Phase transitions and metastability in markovian and molecular systems

  • W. Huisinga, S. Meyn, Ch
  • Ann. Appl. Probab.,
  • 2004
1 Excerpt

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