Asymmetric page split generalized index search trees for formal concept analysis

Martin, B. and Eklund, P. (2006). Asymmetric page split generalized index search trees for formal concept analysis. In: F. Esposito et al, Lecture Notes in Computer Science: Foundations of Intelligent Systems. 16th International Symposium of methodologies for Intelligent Systems, ISMIS 2006, Bari, Italy, (218-227). September 27-29, 2006.


Author Martin, B.
Eklund, P.
Title of paper Asymmetric page split generalized index search trees for formal concept analysis
Conference Paper Type Fully Published Paper
Conference name 16th International Symposium of methodologies for Intelligent Systems, ISMIS 2006    (ERA 2010 Rank C)
DOI 10.1007/11875604_25
Conference location Bari, Italy
Conference dates September 27-29, 2006
Proceedings title Lecture Notes in Computer Science: Foundations of Intelligent Systems  (ERA 2012 Listed)   Check publisher's open access policy
Editor F. Esposito et al
Place published Berlin
Publisher Springer
Publication date 2006
Volume number 4203
ISBN 978-3-540-45764-0
ISSN 0302-9743
Start page 218
End page 227
Total pages 10
Collection year 2006
Language eng
Abstract/Summary 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.
Subjects B1
08 Information and Computing Sciences
Q-Index Code B1

 
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Created: Tue, 14 Aug 2007, 12:36:07 EST