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Fuzzy spectral clustering by PCCA+: application to Markov state models and data classification

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  • Susanna Röblitz
  • Marcus Weber

Abstract

Given a row-stochastic matrix describing pairwise similarities between data objects, spectral clustering makes use of the eigenvectors of this matrix to perform dimensionality reduction for clustering in fewer dimensions. One example from this class of algorithms is the Robust Perron Cluster Analysis (PCCA+), which delivers a fuzzy clustering. Originally developed for clustering the state space of Markov chains, the method became popular as a versatile tool for general data classification problems. The robustness of PCCA+, however, cannot be explained by previous perturbation results, because the matrices in typical applications do not comply with the two main requirements: reversibility and nearly decomposability. We therefore demonstrate in this paper that PCCA+ always delivers an optimal fuzzy clustering for nearly uncoupled, not necessarily reversible, Markov chains with transition states. Copyright Springer-Verlag Berlin Heidelberg 2013

Suggested Citation

  • Susanna Röblitz & Marcus Weber, 2013. "Fuzzy spectral clustering by PCCA+: application to Markov state models and data classification," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 7(2), pages 147-179, June.
    • Handle: RePEc:spr:advdac:v:7:y:2013:i:2:p:147-179
    DOI: 10.1007/s11634-013-0134-6
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    6. Gregory M. Martin & Monica L. Fernández-Quintero & Wen-Hsin Lee & Tossapol Pholcharee & Lisa Eshun-Wilson & Klaus R. Liedl & Marie Pancera & Robert A. Seder & Ian A. Wilson & Andrew B. Ward, 2023. "Structural basis of epitope selectivity and potent protection from malaria by PfCSP antibody L9," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
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