A generalization based hybrid algorithm for clustering semi-structured data
"In this work, a generalized based methodology that combines attribute hierarchy construction, object generalization and data clustering is presented. The algorithm works well on semi-structured data and requires only a minimum of domain knowledge. Since the algorithm reduces the dimensionality of the semi-structured data, clustering of the resulting generalized data often requires less execution time and computer memory"--Abstract, leaf iii.
Ph. D. in Computer Science
University of Missouri--Rolla
xiii, 137 leaves
© 2004 Ming-Yi Shih, All rights reserved.
Dissertation - Citation
Library of Congress Subject Headings
Algorithms -- Computer programs
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Link to Catalog Record
Full-text not available: Request this publication directly from Missouri S&T Library or contact your local library.http://laurel.lso.missouri.edu/record=b5381783~S5
Shih, Ming-Yi, "A generalization based hybrid algorithm for clustering semi-structured data" (2004). Doctoral Dissertations. 1590.