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Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 2001: Advances in Knowledge Discovery and Data Mining pp 186–197Cite as

Representing Large Concept Hierarchies Using Lattice Data Structure

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2035))

Abstract

With the rapid growth in size and number of available databases, the manipulation of large concept hierarchies that cannot be fit in main memory becomes more and more frequent. Several representations of concept hierarchies are possible, for example tree, lattice, table, linked list, etc. In this paper, we propose an efficient implementation technique to manipulate large concept hierarchies. We use a lattice data structure to represent concept hierarchies and encode such a lattice into a boolean transitive closure matrix. A set of lattice operators are defined and implemented as abstract data types on the top of an object-relational database management system, and are used to perform generalization and specialization operations. We show the efficiency of the lattice operators to perform generalization and specialization in large concept hierarchies and compare their performance with the START WITH and CONNECT BY clauses of SQL.

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References

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering, Kasetsart University, Chatuchak, Bangkok, Thailand

    Yanee Kachai & Kitsana Waiyamai

Authors
  1. Yanee Kachai
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  2. Kitsana Waiyamai
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science and Information Systems, The University of Hong Kong, Pokfulam, Hong Kong China

    David Cheung

  2. CSIRO Mathematical and Information Sciences, GPO Box 664, Canberra, ACT 2601, Australia

    Graham J. Williams

  3. Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave., Kowloon, Hong Kong China

    Qing Li

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© 2001 Springer-Verlag Berlin Heidelberg

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Cite this paper

Kachai, Y., Waiyamai, K. (2001). Representing Large Concept Hierarchies Using Lattice Data Structure. In: Cheung, D., Williams, G.J., Li, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2001. Lecture Notes in Computer Science(), vol 2035. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45357-1_23

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  • DOI: https://doi.org/10.1007/3-540-45357-1_23

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41910-5

  • Online ISBN: 978-3-540-45357-4

  • eBook Packages: Springer Book Archive

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