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A Hybrid Cluster-Lift Method for the Analysis of Research Activities

  • Boris Mirkin
  • Susana Nascimento
  • Trevor Fenner
  • Luís Moniz Pereira
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6076)

Abstract

A hybrid of two novel methods - additive fuzzy spectral clustering and lifting method over a taxonomy - is applied to analyse the research activities of a department. To be specific, we concentrate on the Computer Sciences area represented by the ACM Computing Classification System (ACM-CCS), but the approach is applicable also to other taxonomies. Clusters of the taxonomy subjects are extracted using an original additive spectral clustering method involving a number of model-based stopping conditions. The clusters are parsimoniously lifted then to higher ranks of the taxonomy by minimizing the count of “head subjects” along with their “gaps” and “offshoots”. An example is given illustrating the method applied to real-world data.

Keywords

Gene Ontology Similarity Matrix Fuzzy Cluster Spectral Cluster Additive Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Boris Mirkin
    • 1
    • 2
  • Susana Nascimento
    • 3
  • Trevor Fenner
    • 1
  • Luís Moniz Pereira
    • 3
  1. 1.School of Computer ScienceBirkbeck University of LondonLondonUK
  2. 2.Division of Applied MathematicsHigher School of EconomicsMoscow
  3. 3.Computer Science Department and Centre for Artificial Intelligence (CENTRIA), Faculdade de Ciências e TecnologiaUniversidade Nova de LisboaCaparicaPortugal

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