Asymmetric Page Split Generalized Index Search Trees for Formal Concept Analysis

  • Ben Martin
  • Peter Eklund
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4203)


Formal Concept Analysis is an unsupervised machine learning technique that has successfully been applied to document organisation by considering documents as objects and keywords as attributes. The basic algorithms of Formal Concept Analysis then allow an intelligent information retrieval system to cluster documents according to keyword views. This paper investigates the scalability of this idea. In particular we present the results of applying spatial data structures to large datasets in formal concept analysis. Our experiments are motivated by the application of the Formal Concept Analysis idea of a virtual filesystem [11,17,15]. In particular the libferris [1] Semantic File System. This paper presents customizations to an RD-Tree Generalized Index Search Tree based index structure to better support the application of Formal Concept Analysis to large data sources.


Leaf Node Internal Node Index Structure Generalize Index Information Retrieval System 
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 2006

Authors and Affiliations

  • Ben Martin
    • 1
  • Peter Eklund
    • 2
  1. 1.Information Technology and Electrical EngineeringThe University of QueenslandSt. LuciaAustralia
  2. 2.School of Economics and Information SystemsThe University of WollongongWollongongAustralia

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