A comparison of taxonomy generation techniques using bibliometric methods : applied to research strategy formulation
Name
712959766-MIT.pdf
Description
Full printable version
Size
11.42 MB
Format
Adobe PDF
Checksum (MD5)
f6740546d21973ff44472f0220209086
Author(s)
Camiña, Steven L
Advisor(s)
Stuart Madnick and Wei Lee Woon.
Date Issued
2010
Publisher
Massachusetts Institute of Technology
Abstract
This paper investigates the modeling of research landscapes through the automatic generation of hierarchical structures (taxonomies) comprised of terms related to a given research field. Several different taxonomy generation algorithms are discussed and analyzed within this paper, each based on the analysis of a data set of bibliometric information obtained from a credible online publication database. Taxonomy generation algorithms considered include the Dijsktra-Jamik-Prim's (DJP) algorithm, Kruskal's algorithm, Edmond's algorithm, Heymann algorithm, and the Genetic algorithm. Evaluative experiments are run that attempt to determine which taxonomy generation algorithm would most likely output a taxonomy that is a valid representation of the underlying research landscape.
Description
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 86-87).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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