2026-09-20T04:57:32Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/626322022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581com_1721.1_104841col_1721.1_131023col_1721.1_141484
Stuart Madnick and Wei Lee Woon.
Camiña, Steven L
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2011-05-09T15:11:46Z
2011-05-09T15:11:46Z
2010
2010
http://hdl.handle.net/1721.1/62632
712959766
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).
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.
by Steven L. Camiña.
M.Eng.
108 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
copyright. They may be viewed from this source for any purpose, but
reproduction or distribution in any format is prohibited without written
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
A comparison of taxonomy generation techniques using bibliometric methods : applied to research strategy formulation
Thesis
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Publication
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